Bibliographic record
Abstract
A perfect emic understanding of an individual or community other than oneself is impossible; we must nevertheless strive to understand communities on their own terms. Crucial to that is an understanding of a community’s identity; if we are to know them, we must attempt to know how they know themselves. A major ingredient in a community’s identity lies in their understanding of the past, and that is where this paper will concern itself. We are often unaware of the residue of the past, of the centuries of tradition that underlie our actions and our perceptions. The mixing of time goes unnoticed in our daily lives, only occasionally thrust to the surface when we learn some new aspect of history that resounds in our own present lives (Lowenthal, 1985, p. 185). These mostly unconscious, sometimes conscious, implants of the past play a significant role in the construction and maintenance of identity. Before proceeding allow me to lay out some definitions. Terms like memory, heritage, history, relics, and identity are used quite loosely by several different disciplines within the social sciences. For clarity, I will provide working definitions here. For this paper, I consider ‘identity’ to be what an individual or community believes about themselves, what role and characteristics they ascribe to, and consider as distinguishing them from others. Identity making is therefore situated within a view to the internal and the external, and is oft preoccupied with difference making, boundary definition, and comparison.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".