Bibliographic record
Abstract
Since the advent of the Information Highway (Society/Economy) considerable policy-making has been undertaken by governments in Canada and the U.S. in response to the Digital Divide. While measuring the divide has largely been limited to neo-liberal economic analysis, the U.S. appears to be committing more resources and doing more fine-grained analyses of the situation. This paper compares statistical and political economic analyses already completed and provides alternative analyses useful to guarantors of access to information.Depuis l’avènement de l’autoroute de l’information (Société/Économie), un nombre considérable de politiques ont été élaborées par les gouvernements du Canada et des États-Unis afin de réduire le fossé numérique. Alors que la mesure du fossé a été limitée en grande partie par l’analyse économique néo-libérale, les États-Unis semblent engager davantage de ressources et effectuer des analyses plus précises de la situation. Cette étude compare les analyses statistiques et politico-économique déjà achevées et présente des analyses alternatives utiles garantissant l’accès à l’information.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.013 | 0.036 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".