Workplace Literacy Problems: Triangulating on Potential Hot Spots
Bibliographic record
Abstract
Abstract Low levels of literacy skills among workers in the new globalized and information age have been much discussed recently. However, workers' low literacy skills are only a problem if their skills do not meet their job requirements. Auditing workplace literacy job requirements and workers' skill levels is slow, expensive, and possibly conflictual. We propose a relatively simple and inexpensive way for employers to assess the possibility of present and future mismatches between the literacy skills of their own workers and their work. Three experimental checklists, based on literature and experience with a substantial number of actual cases, guide employers to consider their firm's operations concerning the risks of: (a) functional literacy deficits, (b) blocked communication channels on literacy deficits, and (c) management policies discounting literacy issues. Means of synthesizing the information from checklists are offered. Résumé Depuis quelques temps, on parle beaucoup des niveaux d'alphabétisation peu élevés chez les travailleurs de notre nouvelle ère de mondialisation et d'information. Pourtant, ce problème ne devient évident que dans les cas où les compétences de ces travailleurs ne correspondent pas aux exigences de leur emploi. Évaluer les besoins en alphabétisation en milieu de travail est une entreprise coûteuse, lente et pouvant provoquer des conflits. Nous proposons un moyen relativement simple et peu coûteux pour que les employeurs soient capables d'évaluer les décalages présents et futurs entre le niveau d'alphabétisation de leurs travailleurs et leur tâches. II existe trois listes expérimentales, établies à partir de la documentation et de l'expérience et appuyées par des cas réels, pour guider les employeurs à considérer les opérations de leur entreprise en ce qui concerne les risques de: (a) déficits fonctionnels d'alphabétisation, (b) moyens de communication bloqués sur les lacunes en alphabétisation, et (c) politiques de gestion qui ne tiennent pas compte des questions d'alphabétisation. On propose des moyens de faire la synthèse des informations comprises dans ces listes.
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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 | 0.018 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".