Recount: Revisiting the 42 nd Canadian Federal Election to Evaluate the Efficacy of Retroactive Tweet Collection
Bibliographic record
Abstract
In this paper, we report the development and testing of a methodology for collecting tweets from periods beyond the Twitter API’s seven-to-nine day limitation. To accomplish this, we used Twitter’s advanced search feature to search for tweets from past the seven to nine day limit, and then used JavaScript to automatically scan the resulting webpage for tweet IDs. These IDs were then rehydrated (tweet metadata retrieved) using twarc. To examine the efficacy of this method for retrospective collection, we revisited the case study of the 42nd Canadian Federal Election. Using comparisons between the two datasets, we found that our methodology does not produce as robust results as real-time streaming, but that it might be useful as a starting point for researchers or collectors. We close by discussing the implications of these findings.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".