Bibliographic record
Abstract
This paper has two main aims: one is to understand the mechanisms that allow anonymity to facilitate both good and bad ends; the other is to use this understanding to determine the value of anonymity relative to its disvalue across a variety of moral and socio-political domains. Building on previous work in which I characterize anonymity by what I call the ‘central anonymity paradigm,’ I argue here that anonymity is primarily instrumentally valuable as a strategic device to procure some other valued good or set of goods, and is justified derivatively to the extent that it successfully achieves this end. Here, I leave open the question whether anonymity is intrinsically good and, in the final sections, I give reasons why it should sometimes be resisted. My argument proceeds in four stages: first, I describe four domains in which the tensions between the value and disvalue of anonymity can be felt; second, I briefly present the central anonymity paradigm; third, I draw on Kathleen Wallace’s taxonomy of three kinds of anonymity (agent anonymity, recipient anonymity, and procedural anonymity) to show how anonymity is typically achieved in the paradigmatic cases, and why it is so effective at achieving and securing certain ends; and finally, I draw on the concept of “intimate anonymity” from the fields of architecture and urban design as a model for anonymity relations that preserves the individualizing benefits of anonymity—security, privacy, freedom, etc.—while encouraging individuals to form meaningful relations with others that support intimacy, trust and community. I begin with four anonymity tensions.
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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.080 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".