Identity as a compass when navigating uncharted equitable spaces: Our queer evaluation practices
Bibliographic record
Abstract
Abstract Alternative approaches within evaluation increasingly allow space for evaluators to bring themselves to their work. As queers, we are gifted‐partially as a necessity for our survival‐with deeper understandings of and navigational capacities to work within complexity. Furthermore, existing as queer empowers us to think and operate outside what is the norm, known, familiar and comfortable, and thus enables us to challenge normative systems for purposes of social change. Our chapter offers situated insight into what queer evaluation practices look like and empowers us to practice bringing ourselves into different contexts, including uncharted spaces. We illustrate principles of queer evaluation through cases of our unique identities, contexts, landscapes, and evaluation experiences, within a process that is iterative, dialogic, and relational. We argue that the exploration of ourselves is critical as evaluators and invite readers to wander alongside us while actively searching their identities. Rather than hiding these biases and perspectives, we believe in the importance of knowing oneself and our connections to the histories of those who came before, which serve as our guides. Only from this point can we begin to unravel the unknown into the known and transform the inequitable into the equitable that has yet to exist. We argue that by embracing our identities we are better able to navigate the complexities that exist in our work and deepen our understanding of the contexts around us.
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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 | 0.123 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.035 | 0.095 |
| Scholarly communication | 0.030 | 0.022 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".