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 21 September 2026 and assigned it the closest of 31 fixed categories, at 96% 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 why it has no browse page of its own yet.
Growth
6 measurements spanning 28 days, net -1. 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 414–420 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)
Subscribers
Change
3 Sept 2026, 21:19
418
+2
27 Aug 2026, 05:57
416
+1
20 Aug 2026, 13:03
415
-3
13 Aug 2026, 02:37
418
-1
6 Aug 2026, 20:21
419
no change
6 Aug 2026, 19:47
419
first reading
Engagement
20 posts held, back to 1 January 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 1 page of Telegram’s post history, 20 posts per page.
Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 20 posts for this entry, the most recent from 26 July 2026. An engagement rate over an empty window would be a number about nothing.
Reaction mix
88 reactions across 16 posts, in 8 distinct kinds. The most used accounts for 68.2% of them.
Every reaction kind recorded on the sample, most used first
Reaction
Count
Share
Share, drawn
❤
60
68.2%
🕊
11
12.5%
🙏
6
6.82%
❤🔥
3
3.41%
👍
3
3.41%
👏
3
3.41%
👎
1
1.14%
💯
1
1.14%
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 16 of the 20 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 88 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 20 most recent posts we hold, published 1 January 2026 to 26 July 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.
«همایش ملی تاریخ مطبوعات محلی از آغاز تا انقلاب اسلامی »
مطبوعات محلی استان فارس و شهر شیراز تنها صفحات کاغذیِ فراموششده در آرشیوها نیستند؛ آنها سندِ زنده و آینهی تمامنمایِ هویت، اندیشه، زبان، حافظه و تجربه زیسته این دیار به شمار می روند. برگزاری همایش ملی «تاریخ مطبوعات محلی ایران از آغاز تا انقلاب اسلامی» در منطقه جنوب کشور، فرصتی است تا این میراث ارزشمند مورد واکاوی قرار گیرد.
جزئیات بیشتر در فایلِ فراخوانِ پ…
💫*خودت آنالیز کن*💫
✨*کارگاه آموزشی نرمافزارهای آنالیز آماری SAS و SPSS*
🌱انجمن علمی علوم باغبانی و فضای سبز دانشگاه شیراز
با همکاری جمعی از انجمنهای علمی علوم باغبانی کشور و انجمنهای علمی دانشگاه شیراز برگزار میکند
⏰ ۶ الی ۹ تیرماه ۱۴۰۵
🕰️ ساعت ۱۶ الی ۱۸
⭕ بصورت آنلاین
👨🏻💼 مدرس: سجاد رنجبر
دانشجوی دکتری ژنتیک و اصلاح نژاد دام و طیور دانشگاه شهید باهنر کرمان/ سابقه برگزاری بیش از ۵۰ کارگاه آموزشی نرماف…
انجمن علمی کارآفرینی و نوآوری
انجمن علمی علم اطلاعات و دانششناسی دانشگاه شیراز
با همکاری یکدیگر برگزار میکنند:
کارگاه آموزشی نرمافزار:
EndNote
مدیریت منابع پژوهشی و رفرنسدهی استاندارد
مدرس:
👩🏫 سرکار خانم مریم ترکمن
دانشجوی مقطع دکتری علم اطلاعات و دانششناسی
گرایش بازیابی اطلاعات و دانش
📌 سرفصلهای کارگاه:
🔹 آشنایی با محیط نرمافزار EndNote
مقاله
🔹 ایجاد بانک اطلاعاتی منابع پژوهشی
🔹 استناددهی خودکار در ورد
🔹…
انجمن علمی علم اطلاعات و دانششناسی دانشگاه شیراز با همکاری معاونت فرهنگی دانشگاه برگزار میکند:
نشست تخصصی:
سواد رسانه و تنظیم هیجان در شرایط بحران
چگونه از سواد رسانهای برای مدیریت هیجانات در شرایط بحران استفاده کنیم؟
سخنرانان:
👤جناب آقای دکتر جواد عباسپور
عضو هیئتعلمی گروه علم اطلاعات و دانششناسی دانشگاه شیراز
👤جناب آقای دکتر حجت پیرزادی
عضو هیئتعلمی گروه روانشناسی کودکان استثنایی دانشگاه شیراز
📌 محورهای…
انجمن علمی علم اطلاعات و دانششناسی دانشگاه شیراز با همکاری سازمان اسناد و کتابخانه ملی مرکز فارس برگزار میکند:
کارگاه آموزشی:
استناددهی به سبک APA
رفرنسدهی استاندارد برای پژوهش و پایاننامه
مدرس:
👤 خانم دکتر طاهره جوکار
عضو هیئت علمی گروه علم اطلاعات و دانششناسی دانشگاه شیراز
📌 سرفصلهای کارگاه:
🔹 اصول استناد درونمتن (In-text Citation)
🔹 ساختار فهرست منابع (Reference List)
🔹 استناد به کتاب، مقاله، وبسایت، پا…
همچنین بدینوسیله از زحمات و تلاشهای جناب آقای دکتر علیرضا نیکسرشت، در دوران ریاست بخش علم اطلاعات و دانششناسی صمیمانه تشکر و قدردانی میکنیم.
همراهی و حمایت ایشان از انجمن علمی همواره مغتنم و الهامبخش بوده است.
برای ایشان آرزوی سلامتی، توفیق و سربلندی را در همه مراحل زندگی داریم.
با احترام
انجمن علمی علم اطلاعات و دانششناسی دانشگاه شیراز
درود و احترام
انجمن علمی علم اطلاعات و دانششناسی دانشگاه شیراز، انتصاب شایسته سرکار خانم دکتر طاهره جوکار به عنوان رئیس جدید بخش علم اطلاعات و دانششناسی دانشگاه شیراز را صمیمانه تبریک عرض میکند.
برای شما آرزوی توفیق روزافزون، سلامتی و سربلندی را داریم.
بیشک شایستگی، دانش و اخلاق نیکوی شما، سرمایهای گرانبها برای این جایگاه است.
امیدواریم در سایه الطاف الهی، روزهای پرافتخاری برای بخش رقم بخورد.
انجمن علمی علم اط…
❤5👏1
Showing the 12 most recent of 20 posts we hold for @infoscienceshu. 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.
Forward network
Republished by
Channels on the register that have forwarded this channel's posts into their own feed.
Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.
Mentions
Named by 4 registered channels — 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.
Handles this channel named that no longer answer
Dead references
3
handles named in this channel’s posts, vacant today
Evidenced gone
0
we ourselves saw one of these resolve, at some point
Never seen alive
3
vacant every time we have ever looked
@infoscienceshu named 3 handles that resolve to nothing today. That is a fact about the reference, not necessarily a fact about the handle’s history — see the two groups below.
Most of these may never have existed as a live channel at all. A handle a channel names can be a typo, an aspirational name nobody registered, or a channel that was already gone before this one ever mentioned it. Unless a row below is marked evidenced, all we know is that it references a handle that is not a live channel today — not that anything “died”. How this is measured.
Never seen alive
References a handle that is not a live channel — we have no record it ever was one.
@infosci_shirazu named in 2 posts, 8 August 2026 – 8 August 2026
@anjomn_karafarini named in 1 post, 8 August 2026 – 8 August 2026
@horticulture_shirazu named in 1 post, 8 August 2026 – 8 August 2026
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 3 September 2026 — this
entry's latest reading, not the date you are reading this.
“انجمن علمی علم اطلاعات و دانششناسی دانشگاه شیراز” (@infoscienceshu), 418 subscribers as measured 3 September 2026. Telegram Register, tgregister.com/channel/infoscienceshu.
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.