Unpacking the foundational dimensions of work integration social enterprise
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it