Bibliographic record
Abstract
Human beings are meaning makers and beauty lovers; both facts about us are showcased in our adorning practices. In Adorning Bodies, Marilynn Johnson reminds us of the importance of adornment by asking: what do our bodies and the things that we adorn them with mean? Johnson draws from Grice to answer this central question, while taking an interdisciplinary approach to the topic overall. Adorning Bodies raises questions for those interested in the philosophy of language, the philosophy of art, the self and identity, the philosophy of race, the philosophy of biology, archaeology, and sociology (this list is not exhaustive). Johnson is a lovely writer; she is accessible, overtly feminist, and at times quite funny (see her takedown of the beard theory, p. 92–93). Adorning Bodies proceeds in roughly four parts. In Chapters 1–2, Johnson motivates the claim that our bodies and the things that we adorn them with have meaning while showing us why getting at this meaning is difficult. In 2–4 she develops a Gricean framework for understanding the meaning of bodily adornment. Like language, bodies and their adornments have natural and non-natural meaning. In 5–8, she provides a historically and biologically informed analysis of natural meaning in bodies and the connection between bodies and adorning practices. Johnson concludes in Chapter 9 by considering whether and when adornment is art.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.037 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".