Bibliographic record
Abstract
Theories of recognition tend to assume that the archetypal form of recognition is a relationship between two individual subjects. This is because they also assume that only individuals can express recognitive attitudes such as care, respect, and esteem. At the same time, these theories often proceed as if other types of entities can engage in recognition. They may, for instance, claim that a state’s laws give appropriate acknowledgement to some people but not to others. Or they may argue that a social institution – such as a police force or welfare system – may recognize some people but not others. However, there is often a failure to ask who or what is doing the recognition in these cases and others like them. Is it the state, police force, or welfare system which is itself the active agent of recognition? Or is it a group of individuals who are doing the recognition by means of the laws and institutions in question? In this chapter, I want to consider whether another kind of entity can be regarded as playing a similar role in relationships of recognition. Could it be argued that a social environment can recognize some of the subjects who are located in it? Or is such an environment only the means by which one group of individuals recognizes another? I shall explore these questions by considering whether a concept of collective or accumulative harm can be used to understand how an environment may play a part in a network of recognition relationships. And in order to see how these questions play out in practice, I shall consider three specific environments of recognition: Swiss public space affected as it is by a ban on the building of minarets, Quebec’s visage linguistique in which the French language enjoys a certain priority, and the public space of US states which continue to fly the Confederate flag.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.063 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".