Thanks for Stopping By: A Study of “Thanks” Usage on Wikimedia
Bibliographic record
Abstract
The Thanks feature on Wikipedia, also known as “Thanks”, is a tool with which editors can quickly and easily send one other positive feedback [1]. The aim of this project is to better understand this feature: its scope, the characteristics of a typical “Thanks” interaction, and the effects of receiving a thank on individual editors. We study the motivational impacts of “Thanks” because maintaining editor engagement is a central problem for crowdsourced repositories of knowledge such as Wikimedia. Our main findings are that most editors have not been exposed to the Thanks feature (meaning they have never given nor received a thank), thanks are typically sent upwards (from less experienced to more experienced editors), and receiving a thank is correlated with having high levels of editor engagement. Though the prevalence of “Thanks” usage varies by editor experience, the impact of receiving a thank seems mostly consistent for all users. We empirically demonstrate that receiving a thank has a strong positive effect on short-term editor activity across the board and provide preliminary evidence that thanks could compound to have long-term effects as well.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".