Bibliographic record
Abstract
This year the CAIS-ACSI cfp asked us to consider how data are involved in people’s information behaviours, practices, and experiences. This paper responds by drawing on analytical themes and data from completed research on a standard classification system in workplace information practices. I take the view that classification systems are cultural artifacts (Beghtol 2010, p.10) and big data are social artifacts (Ibekwe-San Juan & Bowker 2017, p. 193). Metaphors for occupational data have become naturalized in workplace discourse. Metaphors also contribute to our understandings of information and its role in employment and migration. The research offers alternative readings of these metaphors and proposes ways these address information-centric beliefs in workplace practices.Cette année, l’appel à propositions du CAIS-ACSI nous a demandé d’examiner comment les données sont impliquées dans les comportements, les pratiques et les expériences informationnelles des gens. Cet article répond à l’appel en s'appuyant sur des thèmes analytiques et des données provenant d’une recherche achevée sur un système de classification standard dans les pratiques informationnelles sur le lieu de travail. Je suis d'avis que les systèmes de classification sont des artefacts culturels (Beghtol 2010, p.10) et que les données massives sont des artefacts sociaux (Ibekwe-San Juan & Bowker 2017, p.193). Les métaphores sur les données professionnelles ont été naturalisées dans le discours sur le lieu de travail. Les métaphores contribuent également à notre compréhension de l'information et de son rôle dans l'emploi et la migration. Cette recherche propose des alternatives de lecture de ces métaphores et propose des façons de les utiliser pour aborder les croyances centrées sur l'information dans les pratiques en milieu de travail.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".