Bibliographic record
Abstract
This literature review examines how marginalized students are represented in educational data, addressing disparities faced by student groups such as racialized students, those with disabilities, LGBTQ+ students, students from low-income backgrounds, and those with limited English proficiency. Research on data and data reporting in schools is systematically examined, resulting in the identification of the need for a more holistic approach to gathering and analyzing student data. Approaches such as the integration of student stories and context into data analysis are considered in recommendations for various levels of data use. Recommendations for teachers and classrooms include acknowledging bias in interpretation, aligning outcomes with holistic student goals, and incorporating formative assessment data in classrooms. For administrators and schools, starting the data analysis process with data from marginalized groups, and reinforcing statistics with qualitative data are key. Educational systems can prioritize local needs for improvement, combine accountability with teacher development opportunities, and empower educators to lead school improvement efforts actively. Combined, these approaches to representing and reporting data can improve decision-making in education by recognizing diverse student needs and experiences.
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 | 0.020 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.022 | 0.029 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.029 | 0.008 |
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".