Designing the Tools of the Trade: How corporate social responsibility consultants and their tool-based practices created market shifts
Bibliographic record
Abstract
Combining insights from the sociology of markets and studies of consultants, this article examines the tool-based practices by which market actors enable the agencing of the supply and demand of the market in ways that shape the market’s trajectory. Building on 31 interviews and a rich set of secondary data, we provide an analysis of the development of a market for consultancy products and services for corporate social responsibility (CSR) in the province of Quebec (Canada). Through analytical induction we identified six tool-based practices by which consultants contributed to the agencing of the market, and our results show how these practices collectively created market shifts. Our analysis offers new insights into the processes by which consultants’ tool-based practices produce market shifts, embed environmental and social concerns within market mechanisms, and ‘vascularize’ markets.
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.017 | 0.043 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".