Bibliographic record
Abstract
Bound by Recognition. By Patchen Markell. Princeton: Princeton University Press, 2003. 320p. $59.50 cloth, $19.95 paper. Mainstream views of justice have typically concerned themselves with the distribution of goods such as money, power, opportunity, and liberty. In recent years, however, the traditionally neglected good of recognition has become a major focus of attention in contemporary politics and political theory. Stirred by a growing awareness of the pluralistic character of modern societies, many people now believe that recognition is something that is owed as a matter of justice. Just as poverty and a denial of liberty can have catastrophic implications for a person's well-being and self-development, misrecognition and nonrecognition can demean and insult an individual, leaving him or her with a crippling feeling of inferiority. In response to perceived failures of recognition, identity-related groups have called for significant changes in public policies and institutions: Political debates about everything from the college curriculum to laws regulating marriage, to language rights and race-conscious districting, to institutions of self-government for indigenous peoples and national minorities have been framed as “struggles for recognition.”
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.080 | 0.052 |
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".