Spatial-structural inequalities in education: conceptualizations via the international classification of functioning
Bibliographic record
Abstract
Given the role of education in shaping social ideology, there is value in considering how learning spaces affect students' perceptions of inclusivity and social justice. This paper posits that ability as a central focus in education can intersect with marginalization arising from race, gender, class, and indigeneity, towards potential justification of discriminatory ideologies. The International Classification of Functioning (ICF) is applied as a theoretical framework to conceptualize digital or online learning spaces, with the objective of disentangling how ability confounds inclusive pedagogy. Digital learning spaces may grant marginalized students greater autonomy regarding when and how their unique personal condition is disclosed, so that authentic relationship-building can be fostered without a power hierarchy between peers. Additionally, the ICF illustrates how structural-spatial origins of ability limitations lead to solutions which can present universal benefits to individuals regardless of health condition, serving to eliminate a segregated binary based on a traditional label of deficit/disability. Virtual or remote engagement also helps eliminate transportation barriers associated with accessing the learning space, which may influence unfounded assumptions on effort and legitimacy. In combination, the ICF demonstrates how restructuring of learning spaces emerges with the capacity to remove disabilities, towards enhancing participation in education and in society.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".