Fact Construction and Categorization in Assessment: Cultivating Epistemic Justice and Resistance in Social Work Assessment
Bibliographic record
Abstract
When an individual’s experience is discredited and their views silenced in conversation, epistemic injustice ensues, resulting in an ontological attack on the individual’s human dignity. I examine how social workers claim to know and construct the facts of clients’ experiences, subsequently categorizing them in accordance with professional and institutional knowledge. These constructs may differ from the clients’ own experiences, perpetuating epistemic injustice. Elaborating a process of fact construction and categorization in two case examples, I interrogate the inevitable workings of power at multiple levels during assessment. I argue categorization as a site of epistemic injustice serving three functions: permitting dominant discourses to be taken-for-granted and to legitimize professional actions, framing interactional tasks to align with professional and institutional agendas, and enticing clients and workers with activity-bound accountability, obligation, and entitlement. This analysis invites social workers to reflect critically on how to resist epistemic and social injustice in everyday assessment.
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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 | 0.122 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.013 | 0.091 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".