Bibliographic record
Abstract
This article draws on a detailed case study of a complex decision process in a public healthcare system to consider the role and potential power of numbers in strategizing. Because of their association with precision and accuracy, numbers may seem at first sight to be unlikely tools for decision making in contexts characterized by ambiguous goals and diffuse authority.Yet in the case described in this article, managers successfully mobilized a system of numbers to make an extremely controversial strategic decision.The empirical study examines in depth the micro-practices and processes by which the objectivity and legitimacy of a transparently contestable system of numbers were socially constructed in a public forum. By developing a system whose results mapped on to dominant values and interests, by displaying transparency, consistency and competence in defence of the system, and by organizing the decision process in a way that disempowered adversaries, the pro tagonists in the case were able to infuse a difficult decision with positive value. It is concluded that the power of numbers lies in their ability to fill the strategic void created by pluralism.Though contested, numbers can under certain conditions come to acquire and provide authority in organizations where power is diffuse.This is most likely when the number systems enable the reconciliation of diverse values and interests, when they are embedded in shared systems of meaning, and when they are coupled with and activated by particular micro-practices that support the legitimacy of their promoters as disinterested advocates for the collective good.
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.
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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.040 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".