Bibliographic record
Abstract
How do we evaluate the value of a medical mask? And how does the mask acquire its meanings? In this paper, I approach the mask as a media entity: a mediated and mediating thing whose meanings and values arise from within a complex network of relations. The recent political divide regarding mask-wearing has roots in the ambivalence and confusion about their efficacy in the first few months of the pandemic. It remained unclear for some time whether masks protected the wearer or those around them, but nonetheless a global mask-making cottage industry emerged, shaped by DIY and citizen science. The DIY community cleverly leveraged this core ambivalence, foregrounding multivalence, and thereby feeding into a binary ethical obligation: for whom does one wear a mask? The mask was thus baptized into regular usage by the ‘I/You’ utterance that we are now familiar with: ‘I wear my mask to protect you, as you wear your mask to protect me’. This paper reframes the facemask as a complex media entity , one that absorbs its presuppositions, while also being placed into new arrangements by its arrival through an emerging relational network.
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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".