Bibliographic record
Abstract
Bureaucracy has had few admirers, as a quick perusal of 20th-century political and social theory readily indicates. In recent years, several feminist theorists have also joined this vociferous anti-bureaucracy chorus, denouncing bureaucracy’s excessively hierarchical, impersonal, cold and controlling nature. The goal of this article is to review these charges and to show why the term ‘caring bureaucracy’ is not an oxymoron. In the first two sections, the author considers the various reasons why bureaucratic structures are said to be bad both for the people who work in them (especially women) and for those who deal with them. The author proposes to discuss these charges in light of some research on feminist organizations and street-level bureaucracy (Ashcraft, Due Billing, Dubois). The intention is not to offer a paean to street-level discretion or to the claims of ‘the heart’ in public service; it is, rather, to underscore at once the beauty and the danger of discretion. It is also noted that feminist theorists ought to be cautious when they call for ‘flattened hierarchies’ and for fewer rules in large institutions – for these might work against the best interests of women. The last part of the article offers the outline of a caring bureaucracy and suggests avenues to be explored in future care ethics research.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.105 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| 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".