Cultivating a worldly repose: the contribution of Sally Gadow's work to interpretive inquiry
Bibliographic record
Abstract
This paper discusses the contribution that the work of Sally Gadow makes to understandings of interpretive inquiry and it's potential to inform and influence nursing practice, research, and education. The discussion draws on several of Gadow's published works that make explicit her understandings of what it means to be interpretive, to be open to multiple truths, to hear multiple voices, to have a history, to be experienced, and to recognize agency in language. Situating this discussion of Gadow's contribution in opposition to a metaphysics of genius is intended to move our understanding of particular work past the subjectivity that produced it, past the subjectivized responses to the work, past the reporting on myself - my thoughts, my perspectives, my experiences - to explore, to see the worthwhileness or even the possibilities of exploring the work itself and the worlds it evokes. This paper is a deliberate attempt to disrupt the call to the author to save us from the task of interpreting the questions that the work itself places us under. Gadow's work itself points us away from a valorization of the voice of the author of the work, a single voice, and towards a cultivation of a worldly repose where each interpretive account points us to some longstanding whole to which the work belongs and from which it gains its sense and significance.
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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 | 0.025 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.017 | 0.126 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.001 | 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".