Now more than ever: CITAMS's contributions to a pandemic society
Bibliographic record
Abstract
Each year the Communication, Information Technologies, and Media Sociology section of the American Sociological Association curates a special issue highlighting sociological contributions to technology and media studies. That tradition continued in 2020, even as everything else changed. The articles included in this year’s special issue were mostly written pre-Pandemic, yet their implications seem amplified by the current historical moment. With a globe gone remote, mediated communication rose from a specialist academic subject to an acute social consideration, intersecting with and illuminating basic sociological concerns about inequality, the nature of work, family life, and the compounding effects of race, class, gender, and sexuality as they interplay with social and material conditions. These topics are all reflected in the articles from this year’s issue, now inflected with a post-Pandemic reality that shows insights from CITAMS are needed now, more than ever.
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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 | 0.008 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.032 | 0.017 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".