Bibliographic record
Abstract
In the last decade, archival scholars have begun to deeply reflect upon the experiences of individuals and communities as they interact with administrative and bureaucratic records. They have found that there is a significant gap between the emotional experiences of records activators and the preparedness of archival repositories to address these experiences. Emerging from these realizations is a call for archivists to better understand the experiences of the personal in the bureaucratic and to design and take up reparative, caring, and rights-based frameworks to respond to these previously unaddressed needs. Drawing on semi-structured interviews conducted as part of the author’s master’s thesis, this article maps out connections between transracial, transnational adoptee experiences and ideas about the archival imaginary. In addition to acting as a space for participants to share their stories – which directly demonstrate the ability of records to both create and collapse space for unanswerable questions – this work seeks to take up existing calls to archivists and recordkeepers to consider the impact of the bureaucratic on the personal and to recognize the urgent necessity of addressing these experiences as we move forward into more caring practice.
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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.030 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".