Bibliographic record
Abstract
Christopher J. Schneider explains how he uses Qualitative Media Analysis (QMA) to exploit the unconventional data generated by social media in general, and by (public) Twitter feeds in particular. QMA is a method for analysing systematically the documents (tweets, videos) that are produced by social media platforms, which have changed the ways in which we communicate with each other, and also how we communicate with, and about, organizations. Through social media, therefore, researchers have ready access to a range of novel information that is not easily accessed by other means. The approach is illustrated by a study of police–public relationships in Canada; researchers can find it difficult to access police organizations directly. Twitter gives police organizations a ‘new visibility’ which opens them to greater scrutiny. However, police now conduct ‘image work’ through tweets. This research also explored how police respond to recorded instances of violence and untoward conduct posted on YouTube.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.026 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.019 | 0.023 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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 source (direct Gemma or distilled Codex), 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".