昭和时代的员工旅行就是这样很正常吗?

Channel
吃瓜中心
@CHIGUA1957
On this record: Topic · Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry
14,769subscribers
-33,710 since we began measuring on 7 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
Register entry
| Telegram ID | -1002296608841 |
|---|---|
| Type | Channel |
| Username | @CHIGUA1957 |
| Created | Between 1 September 2024 and 31 March 2025 — estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 7 August 2026 |
| Last confirmed live | 19 September 2026 |
| Measurements held | 34 |
| Confirmed unchanged | 1 time, most recently 19 September 2026 |
| On Telegram | t.me/CHIGUA1957 |
Topic
Memes & entertainment — 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 83% 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
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 19 Sept 2026, 15:02 | 14,769 | -1,828 |
| 17 Sept 2026, 03:01 | 16,597 | -777 |
| 15 Sept 2026, 04:00 | 17,374 | -1,934 |
| 13 Sept 2026, 14:38 | 19,308 | -2,181 |
| 11 Sept 2026, 16:16 | 21,489 | -2,929 |
| 9 Sept 2026, 04:59 | 24,418 | -2,013 |
| 5 Sept 2026, 20:20 | 26,431 | -1,377 |
| 3 Sept 2026, 16:20 | 27,808 | -988 |
| 2 Sept 2026, 08:52 | 28,796 | -812 |
| 1 Sept 2026, 06:14 | 29,608 | -302 |
| 31 Aug 2026, 08:19 | 29,910 | +1,079 |
| 30 Aug 2026, 05:35 | 28,831 | -1,202 |
| 29 Aug 2026, 04:37 | 30,033 | -1,136 |
| 28 Aug 2026, 06:54 | 31,169 | -603 |
| 27 Aug 2026, 03:26 | 31,772 | -773 |
| 26 Aug 2026, 03:12 | 32,545 | -1,080 |
| 25 Aug 2026, 05:56 | 33,625 | -827 |
| 24 Aug 2026, 04:38 | 34,452 | -1,302 |
| 22 Aug 2026, 10:38 | 35,754 | -991 |
| 20 Aug 2026, 22:28 | 36,745 | first reading |
Engagement
349 posts held, back to 7 August 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 59 pages of Telegram’s post history, 20 posts per page.
- ERR · 30 days
- 2.97%
- avg views ÷ 14,769 subscribers
- Avg views / post
- 439
- 150 posts measured
- Reaction rate
- 0.195%
- reactions ÷ views · ER floor
- Posts in window
- 150
- of 349 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 19 of 150 measured posts that carry a reaction reading, and over those same posts' views.
| Window | Rolling 30 days · latest post in window 4 September 2026 |
|---|---|
| Posts held | 349 (7 August 2026 – 4 September 2026) |
| Views total | 65,871 |
| Reactions total | 18 |
| Forwards / comments | not exposed by the public surface — not measured, not estimated |
| Readings taken | 4 Sept 2026, 12:27 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
- 1h 34m
- Average length
- 2m 22s
Measured directly from 40 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
27 reactions across 24 posts, in 9 distinct kinds. The most used accounts for 40.7% of them.
| Reaction | Count | Share | Share, drawn |
|---|---|---|---|
| ❤ | 11 | 40.7% | |
| 🤮 | 4 | 14.8% | |
| 🥰 | 4 | 14.8% | |
| ⚡ | 2 | 7.41% | |
| 🤩 | 2 | 7.41% | |
| 👍 | 1 | 3.70% | |
| 👎 | 1 | 3.70% | |
| 👏 | 1 | 3.70% | |
| 😁 | 1 | 3.70% |
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 29 of the 349 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 27 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.
Measured over the 349 most recent posts we hold, published 7 August 2026 to 4 September 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
不愧是哲学教授 一句话点中要害
🥰1
说个爆论: 中国至少50%的青年女性, 职业都是正在考编, 你就说你麻不麻吧
不懂就问: 遇上这样的邻居怎么办?
贵州工业职业技术大学张超老师出身河南农村,2007年进入贵州工业职业技术大学工作,此后遭遇了长期的不公正对待: 2017年:在学院换届之际,学院党委偷偷摸摸给了他处分,不仅不告知本人,还剥夺了他申诉的权利。 2018年起:教务处几乎年年利用假期,莫名其妙地对他进行通报和整治。 2026年8月3日:在他掏空家底搬新家的当天,人事处领导故意在他搬家前两个小时约谈他,用一些莫名其妙、未公示的事情对他进行“诱骗”和羞辱。 他在视频中情绪激动地反问:如果自己犯法或伤天害理,领导找谈话理所应当,但他自认什么都没做,为什么还要被百般整治和羞辱?最后他无奈表示“人在做,天在看”。 张老师07年进入该学校工作,17年学校给全校老师分配未建成的职工住房,按规定每名老师可得分一套不超过60平方米的房子,但是部分校领导实际上分得了两套房产(名义上套)。张老师向学校党委反映这种集体腐败情况,但一直未得到领导回复,并在此后不断遭受领导的整治与针对。 …
🥰1
命好的人体现在哪方面呢?
❤1
真不文明,怎么提醒她穿上鞋子?
🥰1
上海外滩的和平女神铜像,又称欧战纪念碑,是一战结束以后由英国人建立的,1924年完工,1943年被炸毁了😲 现在网上有人发老照片,在那惋惜,叹息,阴阳怪气…… 因为,它们不知道是被汪伪炸的
🤩2
原来这才是无欲则刚的解读。。
🥰1
这丝袜跟假腿一样
👏1
开超市能干废腰子? 不要骗我
這位兄弟這輩子是走不出去了
Showing the 12 most recent of 349 posts we hold for @CHIGUA1957. 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
Names
Channels on the register whose handles appear in this channel's posts.
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.
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 19 September 2026 — this entry's latest reading, not the date you are reading this.
“吃瓜中心” (@CHIGUA1957), 14,769 subscribers as measured 19 September 2026. Telegram Register, tgregister.com/channel/CHIGUA1957.
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.