Measuring performance: differences between capitalist and labour‐owned enterprises
Bibliographic record
Abstract
Purpose The purpose of this paper is to test whether performance differences between labour‐managed (LOFs) and mercantile (PCFs) firms are due to the measures used in the comparison, rather than to their distinct capital‐ownership configurations. Design/methodology/approach Tests for the equality of two means and two variances of a variety of performance measures were used to ascertain whether differences between LOFs and PCFs firms are due to the measures used in the comparison, rather than to their distinct capital‐ownership configurations Findings The indicators analyzed do not provide either type of organizational structure a definite superiority in either short‐economic performance or in short‐term profitability and the profitability indicators assign as good a chance of survival to LOFs as to PCFs of similar size, even if the analysis of their respective debt structures indicates some clear limitations on their growth prospects. Practical implications The paper stresses the importance of using proper measures of the performance of LOFs, to avoid a common practice of being short‐changed in their evaluation of their economic performance, profitability, return of labour and financial structure. Originality/value The study will be useful to the worker‐owners of the LOFs and to those evaluating their performance, such as lenders, regulators, other public officials and the like.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".