Bibliographic record
Abstract
Food hoarding in human behaviour, on its own, has not been frequently documented or extensively researched within archival studies. Various reasons may contribute to a lack of available information on food hoarding. A primary example is that hoarders often experience feelings of guilt, shame, and dispossession of their hoardings. Accessible materials in food hoarding research, however, do include historically archived propaganda posters from world wars and news articles that described the scarcity mindset of citizens during times of crisis, including wars, genocides, and pandemics. Clinical and sociological research has often provided a general analysis of hoarding that informs researchers of the issues that hoarders deal with in private. However, I propose that research on general hoarding should extend to observing behaviours around food and its interconnection to memory and trauma. Furthermore, I believe that the literature about theories of hoarding as an act of archiving—although there are very few—adds to the perceptions of displaced communities’ experiences with food insecurity and attachment. These new methods of examining the archive explain how the displaced have preserved their pasts, challenged their present through mobility (or lack thereof), and planned for their futures through knowledge beyond a traditional archive.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".