Entangling Reciprocity With the Relational in Narrative Inquiry
Bibliographic record
Abstract
In this article, we develop, through drawing forward fragments of our experiences, a concept of reciprocity as always situated within the relational ontology of narrative inquiry. Reciprocity is most commonly understood within a transactional sense, an exchange of goods. We show important aspects of reciprocity in narrative inquiry, including the importance of intentionally creating and responding to spaces where reciprocity occurs and can be sustained over time and place, and the potential reciprocity holds to change who we, and those with whom we work, are. As we reconsider the ways in which reciprocity is not understood as a transaction in a relational methodology, new questions about the entanglement of reciprocity and recognition emerge. We understand that recognition does not necessarily have to be reciprocal, but recognition is necessary to compose a space where reciprocity can live in our ordinary interactions with others.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.075 |
| Scholarly communication | 0.013 | 0.024 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".