Unpacking the foundational dimensions of work integration social enterprise
Bibliographic record
Abstract
Purpose The aim of this exploratory, mixed methods study was to develop and test a tool that identifies foundational dimensions of work integration social enterprises (WISEs) for use in empirical studies and enterprise self-assessment. Construction of the initial prototype was based upon a review of the literature and prior qualitative research by the authors. Design/methodology/approach A 20-item question pool with a four-point response scale was constructed to explore WISE business and employment practices and strategies for worker growth and development. Three sequential field tests were conducted with the prototype – the first with 5 Canadian WISEs, the second with 14 WISEs in the UK and the third with 6 Canadian WISEs involved in an outcome study in the mental health sector. Each field test included completion of the questionnaire by persons with managerial responsibility within the WISE and evaluative feedback captured through questions on the applicability and interpretability of the items. Findings Testing of the prototype instrument revealed the inherent diversity in the field and the difficulty in creating questions that both embrace that diversity and produce unidimensional variables definable along a spectrum. A number of challenges with question structure were identified and have been modified throughout the iterative testing process. Research limitations/implications This study identified central domains for inclusion in a multi-dimensional WISE assessment tool. Further testing will help further refine scaling and establish psychometric properties. Originality/value This measure will provide a descriptive profile of WISEs across sectors and identify WISE core dimensions for research and organizational development.
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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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".