Memory studies: The state of an emergent field
Bibliographic record
Abstract
The article explores the degree to which memory studies has become established as an academic field. Although we acknowledge that there are drawbacks to formal institutionalization, we contend that it is useful to think strategically about the future of memory studies. We argue that three key developments must take place in order for a field to become institutionalized. First, individual scholars must articulate the field through scientific production and collaboration. Second, higher education institutions must formally recognize the existence of the field through specialized programs and departments. And third, public and private donors must sponsor research via dedicated scholarships and grants. We use these phases as benchmarks in order to assess memory studies’ current state of development. After surveying important writings of key authors in memory studies, we test our assumptions through an online survey with 255 self-identified memory scholars. The results show memory studies to be in a mid-level state of development, where individual agents are the most active drivers of defining the boundaries of the field and driving its further establishment. The major obstacle in this process, identified in both the survey and in the literature review, is the fragmented nature of the discipline, which could be addressed through the pursuit of a more interdisciplinary (rather than multidisciplinary) research agenda.
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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 | 0.070 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.011 | 0.070 |
| Scholarly communication | 0.029 | 0.027 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".