Bibliographic record
Abstract
Property is pervasive, and yet we organization scholars rarely discuss it. When we do, we think of it as a black-boxed concept to explain other phenomena, rather than studying it in its own right. This may be because organization scholars tend to limit their understanding of property to its legal definition, and emphasize control and exclusion as its defining criteria. This essay wishes to crack open the black box of property and explore the many ways in which possessive relations are established. They are achieved through work, take place as we make sense of signs, are invoked into existence in our speech acts, and travel along sociomaterial networks. Through a fictionalized account of a photographic exhibition, we show that property overflows its usual legal-economic definition. Building on the case of the photographic exhibit, we show that recognizing the diversity of property changes our rapport with organization studies as a field, by unifying its approaches to the individual-vs.-collective dilemma. We conclude by noting that if theories can make a difference, then whoever controls the assignment of property – including academics who ascribe properties to their objects of study – decides not only who has or who owns what, but also who or what that person or thing can be.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.045 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".