Flexible Small Firms? Why Some Small Firms Facilitate the Use of FWPs
Bibliographic record
Abstract
Abstract. This paper examines why some small firms offer Flexible Workplace Policies (FWPs) while others do not and what factors contribute to the offering and use of FWPs within small firms. A multiple case study is employed using multiple data sources on seventeen information technology (IT) small firms in Canada. Findings reveal three types of firms with regard to their flexibility, working hours, and approaches to time. Among these firm types, discernible patterns emerged based on the owners’ past employment experiences and personal approaches to work-life balance. Our results suggest that structured social relations experienced through past places of employment have lasting effects on small firm owners in their current firms. Résumé. Cet article examine la raison pour laquelle certaines petites entreprises proposent des politiques de lieu de travail flexible alors que d’autres ne les offrent pas; l’article se penche aussi sur les facteurs contribuant à la prestation et à l’utilisation de ces politiques par les petites entreprises. Une étude de cas multiples est utilisée, faisant appel à des sources de données multiples de 17 petites entreprises de technologie de l’information (TI) au Canada. Les conclusions révèlent trois types d’entreprises en ce qui concerne la flexibilité, les heures de travail et les approches du temps. Certains modèles visibles se dégagent parmi ces types d’entreprises selon les expériences antérieures et les approches de conciliation travail-vie du propriétaire. Nos résultats indiquent que les relations sociales structurées vécues dans des lieux de travail antérieurs ont des effets durables sur les propriétaires de petites entreprises dans leur fonction actuelle.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".