Channel
@english_learning_discuss
On this record: Topic · Observations · Also posting the same content · Growth · Engagement · What this channel posts · Posts · Citations · Cite this entry
11,258subscribers
-5 since we began measuring on 6 August 2026
Risers and fallers across the register · movement among entries of 10,000–31,623.
| Telegram ID | -1001414226421 |
|---|---|
| Type | Channel |
| Username | @english_learning_discuss |
| Created | Between 1 April 2019 and 31 August 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated. |
| First recorded | 6 August 2026 |
| Last confirmed live | 12 August 2026 |
| Measurements held | 6 |
| Confirmed unchanged | 1 time, most recently 12 August 2026 |
| On Telegram | t.me/english_learning_discuss |
Politics & activism — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-UD-Q6_K_XL, prompt version 1) read this channel’s own recent posts on 10 August 2026 and assigned it the closest of 31 fixed categories, at 99% 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.
These are measurements, not verdicts. Each one below states something we counted, alongside the evidence it was counted from, so you can check it rather than take it. None of them is graded: every observation this register holds is recorded at severity 0, because the precision of the detectors behind them has not been measured yet, and a rating we cannot support is worse than none. Read each as a fact about the data, not as a judgement about the channel. How we measure.
Posts published here appear word for word on 1 other registered channel. The matching is on the text itself, not on Telegram’s forward marker, so it finds a copy whether or not it was labelled as one.
| Posted first | Then | Overlap | Gap |
|---|---|---|---|
| @english_learning_discuss/28441 · this entry6 May 2026, 11:59 UTC | @labor_one/129557 May 2026, 05:57 UTC | 1.00 | 18 hours |
| Channel | Matching posts | Text overlap | Typical gap | Published first |
|---|---|---|---|---|
| @labor_one | 5 (5/5 hand-verifiable sample passed) | 1.00 | 43 hours | this entry (5–0) |
Text overlap is the Jaccard coefficient over the set of distinct three-word phrases in the two bodies: 1.00 is identical wording, and the threshold for counting a pair at all is 0.70. Candidates are generated by simhash LSH (4 x 16-bit bands, exact Hamming <= 3) verified against the bodies with Jaccard over the SET of distinct 3-word shingles. Published first counts which side of each matching pair carries the earlier timestamp — in this corpus, which is the limitation directly below.
What this cannot establish
MEASURED, DOMINANT ERROR SOURCE: a post ingested before 2026-08-06 may have carried a forward header that was not recorded. A 45-pair hand-check against live t.me pages found 14 (31%) where the live page shows a forward header naming the other channel and the database has none, plus 4 more (9%) naming a third party. The text match itself was wrong 0 times out of 45. Read attribution_capture.items_in_trusted_window before treating the unattributed count as a claim.
Telegram lets a channel forward a post with a header naming the source, and we only began reliably recording that header on 2026-08-06. None of the 0 matches recorded here fall after that date, so for this entry we cannot say whether any of them carried a credit. The duplication is measured; the absence of attribution is not.
“Published first” means first in this corpus. We hold 11 comparable posts for this entry, running 4 May 2026 to 8 May 2026. A channel we have read one page deep will look younger than a neighbour we have read in full, and the order would flip with no change in the underlying facts.
The detector’s own notes on this observation, as it recorded them. Names in this_style are fields of the underlying evidence record, which the plain-English paragraphs above read out for this entry.
Across the whole group of 2, the earliest publisher we hold is @english_learning_discuss — which is this entry. That is a statement about our reading window, not a claim of authorship.
Recorded under the key clone_source, last confirmed 7 August 2026. An observation that a later pass no longer finds is cleared, and a cleared observation is removed from this page rather than being shown struck through — we do not keep publishing a claim we have withdrawn. Dispute an observation.
This channel’s posts match, word for word or near enough, posts on 1 other registered channel, found by comparing text fingerprints across every channel on the register. That matching has been checked by hand against the live Telegram pages and found reliable — 0 wrong of 45 pairs re-read.
Which channel, if either, published first is deliberately not shown. The same hand-check found that reading wrong 18 of 45 times — 60%, no better than a coin flip — because it depends on how deep our own crawl happened to reach into each channel’s history, not on when the content was actually first posted. This list is ordered by subscriber count, the same as every other listing on this site, never by which channel we think came first. Word-for-word matching has several ordinary explanations besides copying — a channel mirroring itself, an unattributed repost, or two channels independently repeating the same wire story — and this measurement cannot tell those apart. How this is measured.
| Measured (UTC) | Subscribers | Change |
|---|---|---|
| 12 Aug 2026, 21:03 | 11,258 | +1 |
| 10 Aug 2026, 14:51 | 11,257 | -4 |
| 9 Aug 2026, 16:08 | 11,261 | -1 |
| 8 Aug 2026, 17:36 | 11,262 | +1 |
| 7 Aug 2026, 14:32 | 11,261 | -2 |
| 6 Aug 2026, 20:46 | 11,263 | first reading |
15 posts held, back to 4 May 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 15 pagesof 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 15 posts for this entry, the most recent from 8 May 2026. An engagement rate over an empty window would be a number about nothing.
Measured directly from 1 video 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.
我主张的女权主义，不是什么“女性应该和男性一样被征兵”，而是“没有任何性别的人应该被征兵因为征兵违背了基本的身体自主权”。 强制征兵这件事情本就不该存在。没有任何人有权利强迫另一个人去冒生命危险。如果没有足够多的人愿意自愿加入当前的战事，那说明这个战事本来就不该继续打下去了。既然政府没有权利强迫民众捐献自己的肾脏，那也不应该有权利强迫民众冒着生命危险去参加一场Ta们本人根本不同意或者根本不关心的战事。 很多人夸赞以色列，说以色列不仅强制男性服兵役，也强制女性服兵役，做到了包容和平等。包容平等让每个人都有机会屠杀巴勒斯坦人？包容平等让每个人都有机会为自己并不同意战事“献身”？真是见鬼了。 source
不允许跨性别孩子进行性别转换并不会让他们变得“正常一点” ，那只会让他们痛苦和焦虑。 source
优步：要遵守法律？那我们还怎么赚钱？ 谷歌：不让垄断？那我们还怎么赚钱？ 雀巢：不让压榨劳工？那我们还怎么赚钱？ openAI：不让窃取信息？那我们还怎么赚钱？ （与此同时） 媒体：在超市偷东西的小偷是否应当判处死刑，以儆效尤？ source
今天是五一劳动节，是全世界劳动者的节日。 我在想我们这些有身心障碍的人。我们中很多人因为工作而落下了残疾。我们中很多人完全不能工作了。我们中也有很多人正在工作，却不得不面临工作环境中的刁难。还有很多公司随意打压拖欠我们的薪资... 无论你处于什么情况，无论你遭遇过什么，我都为你还坚强地活着而高兴。你努力活在这个世界上，就是对我们这个世界的不公正的反抗。 source
在还未成年的时候，我曾经尝试过逃离家庭，却发现自己根本无法获得任何社会救济，只能在走投无路之后再次回到虐待我们的父母的身边。因此当看到有人故意曲解“儿童解放”这个概念的时候，我真的很生气。我们应该建设一个能允许曾经的我离开家庭获得庇护、为我自己和我弟弟的安全争取保障的社会，而这，就是“儿童解放”。 在这样一个社会里，遭到忽视的孩子们有权为自己争取必要的医疗服务。 在这样一个社会里，孩子们能接受到“如何为自己争取权利”的相关教育，学会如何为自己发声，有效表达自己的需求和意愿。 在这样一个社会里，所有人都必须以平等的态度对待孩子，尊重孩子的身体自主权，保障孩子不被剥削和虐待的权利；家长无权强迫孩子接受不必要的医疗干预（如强迫间性人孩子接受“性别矫正”），也无权禁止孩子接受医疗服务；同时，家长无权以“在家教学”为名隔离或监控孩子。所有从家长手中夺回的权利，必须回归孩子本人，而不是交由任何其它人代为行使。 “儿童解放”不仅仅是确保孩子…
我真的很喜欢公交车直直地开过来，恰好停在我面前，然后缓缓打开车门的感觉。 龙来了。龙在我的面前停下来。我是龙选中的人。 source
了解生活的最好方式，是去热爱世间诸多的美好事物。 source
我觉得每个人都该思考这个问题 —— 当我看到什么样的人物形象的时候，我会觉得那一定是个心地残忍的人？ 在回答完这个问题之后，紧接着再问自己一个问题 —— 我这样的想法是哪里来的？ ❶ 如果在英雄片里，那个身体有残缺的形象一定是大魔头，那为什么经历过同样险峻的战斗的主角却从来不会因为战斗负伤而失去一条胳膊，或是一条腿，或是一只眼睛？ ❷ 为什么在科幻片里，反派的肤色都更黑？而主角的肤色都更白？ ❸ 为什么影片里贪婪成性的老板总是更胖？而主角总是更瘦？ ❹ 为什么谍战片里，反派的军师总是伤痕累累，而因为过去的战斗留下了深重心理阴影的主角却没有留下任何伤疤？ ❺ 为什么伺机害人的变态总是长着胡子化浓妆穿裙子的人？为什么从来没有跨性别女性成为主角？ ❻ 你打算维持这样的偏见吗？ ❼ 你打算让你自己创作的作品也充满这样的偏见吗？ 不要不加反思把你耳濡目染造成的偏见再次写进你的小说或同人文里。 另外，我看到评论里有很多人提到了影视文…
请允许我再次为大家敲响警钟： 让我们把关注点放在“这件事情有没有伤害到别人”上，而不是“这件事是不是很恶心”上。 如果你不确定该怎么看待某个事情，又或者如果有人义正严辞告诉你某个你一时间想不明白的复杂事情该如何如何看待，然后让你“别想了赶快行动”，请一定要停下来问问自己，“这件事情有没有伤害到别人”。 你的做法是不是真的让那些受到伤害的人免受了至少一部分伤害？这些伤害是不是真实存在的伤害？如果你的做法减少了“某个假设情形下有人受伤害的可能性”却给另一些人造成了实实在在的伤害，那十有八九你是受到了某种宣传洗脑的误导，不小心成为了加害者的走狗。 如果你接受到的信息不断在和你说，某些事情很恶心所以你必须怎么怎么做，那些信息很可能是要把你推向极端。如果你被激起了某种情绪所以要去做某个事情，那你很可能是被操纵了。很遗憾，现在不是90年代了，社交媒体的兴起早已把诚信消磨殆尽了。如今，你信以为真并加以转发的帖子里很可能至少一半是完全虚…
有些人觉得自己正在努力走出过去的创伤，但实际上却只是一味回避所有能联想到过去创伤的人和事情，让自己陷入孤独之中。 这是一种通过回避来应对创伤的方式，这种应对方式并不能让人完全走出创伤。而想要真正走出创伤，我们必须去建立能改变过去的创伤反应的新情感联结和新人际关系。 当同样的情形下，我们一次次遇到和早先创伤时期不同的对待，我们的情绪反应系统也会慢慢开始改变。 有人没有选择抛弃我们，而是选择来寻找我们的时候；有人没有选择无视我们，而是选择努力理解我们的感受的时候 —— 我们的神经系统会发生变化，我们下意识里对世界认知也会开始重新书写。 source
你吃得越少，喝得越少，读得越少；你去剧院越少，去舞厅越少，去酒吧越少；你想得越少，爱得越少，思考得越少，唱歌越少，画画越少，运动越少；你存下的就越多，你坚不可摧的资产就越多。 然后你的存在变得越来越贫乏，你的生活变得越来越无趣，你的生命里真正表达出自身价值的部分越来越萎缩。你拥有的越多，你的生命就离你越远，你的内心就离你越远。 —— 卡尔·马克思，1844年 source
去年，我看到有人身后跟着一只很大的大雁宝宝。我拦住了他，问他知不知道有一只大雁宝宝一直在跟着他走。他说他当然知道，他是有天在路边发现了这只被大雁妈妈遗弃的大雁宝宝，然后就把这只大雁宝宝抱回家了。他说他会把大雁宝宝照顾到飞走的一天 —— 我：呃，这只大雁宝宝应该不会飞走了... 他：它会的，我已经为它将要飞走而难过了。 我：大雁虽然谈不上友善，但它们认定你就不会离开，你看它现在不用拴绳就会跟着你走来走去，它应该是跟定你了。 我看到这一人一雁走来走去已经一年多了。这个人经常会和这只叫“鸭鸭”的大雁一起去公园，这样“鸭鸭”就可以在公园里捉虫子吃了。 我这周又在我们这边的小店边上看到“鸭鸭”了！这一人一雁经常会去我们这边的小店买啤酒，然后去公园。大雁没有离开！我想这段关系可能不仅是这只大雁最长久的关系，也是这个人最长久的关系。 我忘记说了，这只大雁会啄人！因为你懂的，大雁就是这样。我尝试给这只大雁吃点南瓜籽，结果它无情地做出了要…
Showing the 12 most recent of 15 posts we hold for @english_learning_discuss. 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.
Citation-graph rank — 969,450 of 1,151,006entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.
Republishes
Channels on the register whose posts this channel has forwarded.
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.
Named by 2 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.
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 12 August 2026 — this entry's latest reading, not the date you are reading this.
“英文学习桌” (@english_learning_discuss), 11,258 subscribers as measured 12 August 2026. Telegram Register, tgregister.com/channel/english_learning_discuss.
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.