With a Little Help from Our Friends: Applying a Critical Friends Orientation to Critical Literature Reviews
Bibliographic record
Abstract
ABSTRACT This paper describes how we developed and applied an exploratory critical friends orientation to a critical literature review that explores how feminist theories and approaches have been used in archival studies literature and reports on insights generated by this method. A critical friend is a trusted ally and critic who both values our ideas and can push them forward. Our “critical friends” critical literature review includes two parts; using traditional critical review methods we identify and synthesize how critical feminist approaches have been employed in archival studies literature. Atypically, and in part two, we also pay attention to those scholarly articles that discussed relevant or related concepts but were ultimately excluded from our final literature review corpus during the appraisal process. These peripheral articles act as critical friends to the research area under review. We describe how this approach identifies disciplinary boundaries and traditions and explores areas of overlap across intersecting and adjacent fields. A critical friends approach allows us to generously interpret and analyze the complex concepts of “feminisms” and “archives” across disciplinary fields in order to identify, learn from, and engage across fields that have much in common as well as fundamental differences.
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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 | 0.272 | 0.375 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.022 | 0.008 |
| Science and technology studies | 0.022 | 0.052 |
| Scholarly communication | 0.029 | 0.034 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".