An order of distinction (or, how to tell a collection from a hoard)
Bibliographic record
Abstract
What is the difference between a collection and a hoard? This article draws upon an array of sources – from the DSM-V and current psychiatric research on hoarding, to recent media stories and artist Song Dong’s Waste Not (2009), to the author’s own participant observation with the Toronto Hoarding Coalition and the 21 ethnographic interviews she conducted with professional home organizers in the Greater Toronto Area between 2014 and 2015 – to examine how popular and psychiatric discourses that distinguish collecting and hoarding reveal a complex set of rules about what constitutes the healthy and moral ordering, organization and arrangement of one’s material possessions in contemporary life. In an age of seemingly limitless possibilities for accumulation, the author argues that it is not just the fact of having things that stands as a matter of distinction. One must also demonstrate an active engagement in practices related to the curation and management of one’s object world.
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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.057 |
| Scholarly communication | 0.011 | 0.027 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".