Bibliographic record
Abstract
Higher education institutions are increasingly aware of the importance of inclusive assessment, yet large-scale implementations of inclusive assessment policies and practices are rare. Why is it so tricky to design assessment that inclusively considers the diversity of students? This article argues that whilst trying to solve the problem of inclusive assessment, research and practice communities may have forgotten to pay attention to this problematisation itself. Perhaps the problem of inclusive assessment cannot be solved. In this article, inclusive assessment is theorised as a paradox that is organised around three central tensions: (1) Whereas assessment aims to reduce human diversity to hierarchies and categories, inclusive education seeks to move beyond such sorting systems; (2) whereas assessment relies on uniformity, inclusion builds on diversity; (3) whereas assessment is grounded in individualism, inclusion grows from interdependence. Inclusion and assessment may simply be incompatible, at least in terms of how these ideas are currently understood. It is suggested that higher education sectors may continue living with the paradox, try to mitigate the paradox, or embrace the paradox. Given that students are the ones who must live through this paradox, these issues are not merely theoretical but urgent and real.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.066 | 0.113 |
| 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 | 0.047 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.006 | 0.014 |
| 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; 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".