Education — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 10 September 2026 and assigned it the closest of 31 fixed categories, at 100% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and the other topics.
Growth
59 measurements spanning 90 days, net -45. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 20,823–21,310 and does not start at zero.
Measurement log — every subscribers count we have recorded, most recent 20 of 59
Measured (UTC)
Subscribers
Change
5 Nov 2026, 05:29
21,162
+1
4 Nov 2026, 06:34
21,161
-14
3 Nov 2026, 02:47
21,175
-5
2 Nov 2026, 03:04
21,180
-7
1 Nov 2026, 02:07
21,187
-19
31 Oct 2026, 05:19
21,206
+12
30 Oct 2026, 01:48
21,194
+80
29 Oct 2026, 04:58
21,114
+14
28 Oct 2026, 02:47
21,100
-10
27 Oct 2026, 02:00
21,110
-7
26 Oct 2026, 01:51
21,117
-9
25 Oct 2026, 01:18
21,126
+2
24 Oct 2026, 02:46
21,124
-1
23 Oct 2026, 00:37
21,125
-3
22 Oct 2026, 00:05
21,128
-10
20 Oct 2026, 16:55
21,138
-17
19 Oct 2026, 17:46
21,155
-17
18 Oct 2026, 20:54
21,172
-13
17 Oct 2026, 18:56
21,185
-11
16 Oct 2026, 15:36
21,196
first reading
Engagement
79 posts held, back to 28 July 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 68 pages of Telegram’s post history, 20 posts per page.
ERR · 30 days
1.52%
avg views ÷ 21,162 subscribers
Avg views / post
322
17 posts measured
Reaction rate
2.04%
reactions ÷ views · ER floor
Posts in window
17
of 79 held
ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.
ER is defined industry-wide as (forwards + reactions + comments) ÷ views — note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate. It is computed over the 13 of 17 measured posts that carry a reaction reading, and over those same posts' views.
What these figures were computed from
Window
Rolling 30 days · latest post in window 4 November 2026
Posts held
79 (28 July 2026 – 4 November 2026)
Views total
5,469
Reactions total
77
Forwards / comments
not exposed by the public surface — not measured, not estimated
Readings taken
5 Nov 2026, 02:48 UTC
Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.
Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.
Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.
What this channel posts
Video runtime
5m 36s
Average length
1m 52s
Measured directly from 3 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no ≈ mark.
Reaction mix
579 reactions across 72 posts, in 11 distinct kinds. The most used accounts for 47.5% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
275
47.5%
🔥
131
22.6%
👍
126
21.8%
👏
14
2.42%
💯
9
1.55%
❤🔥
8
1.38%
😍
6
1.04%
🎉
5
0.864%
👌
2
0.345%
🥰
2
0.345%
👎
1
0.173%
No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.
Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.
Coverage. Reactions were read on 73 of the 79 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 579 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 79 most recent posts we hold, published 28 July 2026 to 4 November 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.
Recent posts
Telegram’s own ‘edited’ marker: 0 of 17 posts held from the last 30 days (0%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).
🎁 Почти 50 лучших каналов о карьере, HR и управлении — в одной папке. И подарки к ней
Знакомо?
😩 Резюме разослано, а приглашений на собеседование нет
😩 Ты руководитель, а команда работает не так, как хочется
😩 Нужно расти в доходе и должности, но непонятно, с чего начать
😩 Подписан на 100 каналов, а пользы мало
Мы с топовыми экспертами собрали папку, которая заменит десятки курсов 👇
📂 Что внутри:
💼 Карьера и пои…
Почему привычный план статьи не работает
и как это исправить с помощью матрицы
Типичный план статьи, который присылают мне студенты: «Введение. История вопроса. Современное состояние вопроса. Основные проблемы вопроса. Основные решения вопроса. Заключение».
Допустим… Но если я отправлю писать по такому плану, знаете, что будет?
Ничего. Ближайшее время автора я не увижу, а если стану спрашивать «как дела», услышу чт…
Светофор задач для ИИ в научных статьях
Давайте признаем, нейросети больше не экзотика. Аспиранты и преподаватели используют их для генерации идей, редактуры и даже написания черновиков. Но в научном мире есть четкая грань между «умным помощником» и «нарушением академической этики».
Чтобы не получить отказ от редакции и не потерять репутацию, используйте простое правило «Светофора». Показала его суть в карточках.
…
4 привычки, которые появляются после обучения на курсе «Научная статья за 7 шагов»
✍️ Не ждать вдохновения
Авторы ждут, когда «текст сам пойдет». Авторы, которые публикуются, формулируют конкретный исследовательский вопрос и собирают вокруг него факты. Статья для них — логичный ответ на задачу, а не творческий порыв.
✍️ Строить структуру
Попытка начать с введения — самый быстрый путь к тупику. Публикующиеся авторы …
Можно ли превратить написание статей в привычку?
Я и кураторы курса «Научная статья за 7 шагов» поддерживаем связь с выпускниками. Поэтому у нас есть возможность спросить, изменилось ли что-то в их публикационной активности. Довели ли они статью на курсе до публикации? Как пишут теперь?
В их ответах я увидела прекрасную тенденцию — многие авторы теперь публикуются гораздо больше и регулярнее, чем раньше.
⭐ Курс ра…
«Теперь есть журнал-мишень, тема, исследовательский вопрос, тип и план научной статьи, а главное — понимание и вдохновение»
Это результат очной встречи «Пишем статью ВАК с ИИ». Если вы тоже хотите ощутимого продвижения в своей научной статье и вдохновения для работы, приходите на онлайн-лекцию «Научная статья: 7 шагов к готовому тексту».
Встречаемся 5 ноября в 10, 14 и 19 часов мск. Ссылку для входа пришлю всем зар…
Выбрали журнал — что дальше?
Как параметры издания влияют на структуру и подачу
Я раз за разом настаиваю на том, чтобы авторы выбирали журнал ДО того, как будут писать текст. А зачем это нужно делать?
Ответ прост: выбор издания сильно влияет на то, каким будет ваш текст в итоге. Ведь никому не хочется делать двойную работу и сначала писать текст «вслепую», а потом подгонять его под стандарты редакции.
На что влияе…
[бесплатно] 5 ноября сделайте 7 шагов навстречу к готовому тексту
В 10, 14 и 19 часов мск проведу повторы лекции по научным статьям
Вчера на очной встрече мы заложили основы написания научной статьи ВАК: разобрались, как выбирать журнал, подобрали издание и оценили его параметры, убедились, что оно подходит и соответствует требованиям. А еще составили подробный, развернутый план работы, основанный на исследователь…
Коротко о том, как сегодня поработали на очном интенсиве🔥🔥🔥 Ещё будут фото, видео и отзывы!
Каждый раз перед встречей волнуюсь, и думаю, зачем я это придумала🙈 Но результаты и благодарности участников встречи говорят мне: "Продолжай!" Так что до новых встреч! 😍
Хорошие новости! Чем запомнился этот месяц и что будет дальше
Я уверена, что рефлексия — обязательная часть любой работы. Без нее кажется, что ничего не сделано, хотя на самом деле так много всего успели. Ну и, конечно, это способ проверить, ничего ли вы не забыли.
В школе «Конструктор научной карьеры» мы успели много.
Запустили два курса: по искусственному интеллекту и по высокорейтинговым публикациям. Участники …
Промпт для нейросети не дает научного результата
Смотрите видео
Просите нейросеть написать статью, получаете красивый текст, но уже через минуту понимаете, что в рукопись его вставлять нельзя? В видео рассказала, почему так происходит и как это исправить. А также объяснила, зачем нужны роли в промптинге.
Сегодня этичное использование нейросетей мы будем разбирать глубже с участниками очной встречи. Но если вы по к…
Самыми активными участниками наших живых встреч, по традиции, являются члены Клуба академической карьеры
И это неудивительно: когда постоянно находишься среди единомышленников, обсуждение не заканчивается вместе с мероприятием. Это наши «амбассадоры», которые уже знают, сколько полезного дает участие в сообществе.
Если вам тоже хочется быть частью поддерживающего сообщества, в котором есть ответ на каждый вопрос, п…
❤2👍1🔥1
Showing the 12 most recent of 79 posts we hold for @kireeva_science. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked ≈ was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.
Posts edited after publishing
@kireeva_science edited 1 post after it first published — the same permalink now carries different wording than the one this register originally read, caught because our own crawl held a copy of the earlier text.
An edit is not deception. Typo fixes, price updates and corrections look exactly like this too — this register can tell you the wording changed and when, not why. How this is measured.
First edit seen
14 August 2026
Most recent edit
14 August 2026
Posts recorded as deleted after publishing
We recorded 8 posts on @kireeva_science as deleted — posts this register held that were missing from the channel’s public page on two separate reads, taken from different network addresses at least an hour apart.
A deletion is not wrongdoing. This register can see that a post it held is no longer on the channel’s public page, and when — not why. Expired ad placements, reposts, rotating links and corrections all look the same. This is a count of recorded detections, not an exact total: it misses deletions we could not see, because only a post we held and later re-read inside the page can be seen to go, and it may include a small number of wrong records. How this is measured.
First seen missing
14 August 2026
Most recent seen missing
1 September 2026
Median time before seen missing
3 days
Seen missing in the last 30 days
0
Mentions
Named by 1 registered channel — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.
Named by
Channels on the register whose posts name this channel's handle.
A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.
Channels Telegram recommends alongside this one
Telegram’s own answer, not this register’s. When this register asks Telegram’s API what is similar to this channel, this is the list it returns, in the exact order Telegram returns it — never re-sorted by subscribers or by anything else this register measures. The relationship, and the order, are Telegram’s; we record them and date them, and make no claim of our own about which of these channels actually resemble this one.
Диссертация | Профессор Воронцова @disserlight · 15,112#1
Artist.ru – канал для ведущих, организаторов, певцов, танцоров, музыкантов @artist_ru · 3,141#96
Read from Telegram’s recommendation API, most recently 25 September 2026. Telegram holds a list like this for a small and growing share of the register — how this is measured, and why most channel pages show nothing here.
Appears in Telegram’s recommendations for other channels
The reverse of the list above, and a different kind of signal. This does not require this channel to have ever been asked about directly — each row below is a channel we DID ask Telegram about, whose Telegram-generated list happened to include this one. A channel can appear here with an empty list above it, because being named by someone else’s query is independent of having been queried itself.
Научные конференции РИНЦ @konferen · 34,071 Telegram ranks this channel #10 of 94 here — alongside 93 others — read 14 September 2026
Гуманитарные конференции @confhum · 26,116 Telegram ranks this channel #60 of 88 here — alongside 87 others — read 14 September 2026
факультет @studyfaculty · 43,583 Telegram ranks this channel #67 of 97 here — alongside 96 others — read 5 September 2026
This channel appears in 3 seed channels' Telegram-generated recommendation lists in total. Each is Telegram’s list for THAT channel, not this one — see how this is measured.
Cite this entry
A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 5 November 2026 — this
entry's latest reading, not the date you are reading this.
“Диссертация | Наталья Киреева | О научных статьях и диссертациях” (@kireeva_science), 21,162 subscribers as measured 5 November 2026. Telegram Register, tgregister.com/channel/kireeva_science.
Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.