Good practice on inclusive curricula in the mathematical sciences
Bibliographic record
Abstract
Introduction: good practice on inclusive curricula in the mathematical sciences Good Practice on Inclusive Curricula in the Mathematical Sciences with developing effective written and oral communication of MSOR material with both peers and staff.This might include, for instance, use of LaTeX, a format used for the typesetting of scientific documents.However, no single format has yet emerged which can be read or transformed to be read effectively by all and this barrier is a recurring theme throughout the contributions to the guide. Differing perspectivesA student may draw on support from needs assessors, assistive technology trainers, disability advisers, specialist mentors and study skills tutors, librarians, careers advisers, study support, examinations support and document conversion staff.E-learning specialists and computing services may be responsible for ensuring access to the virtual learning environment, computer systems and software.Most of these support professionals will not have substantial experience of mathematical subjects and, not unreasonably, may assume that generic approaches to access and inclusive design remain valid.For example, it may be incorrectly assumed: that all electronic resources are accessible; that Braille, large or alternative print and speech formats can be produced automatically; that staff will typically provide documents in editable electronic formats; that standard optical character recognition and voice recognition software works; that students will know how to use software such as literacy support and mindmapping programs when faced with a proof or partial differentiation question; and, that standard study support tutorials will be effective.Meanwhile, lecturers and tutors in mathematics, unlike their counterparts at a specialist school, are likely to have only limited knowledge in the domains of the support professionals listed above.Not unreasonably, they may assume that the student has been provided with assistive technology, training, human support and advice appropriate to the specialist nature of their studies and the ways in which MSOR content is communicated.Understanding of the nature of mathematics, how it is communicated, taught and assessed, rests with the subject department.The contributions to the guide evidence the value of support professionals developing some understanding of the specialist nature of mathematics and of departments developing their technical and pedagogic offering in awareness of access challenges.This leads to the recommendation that students, MSOR staff and support professionals should collaborate to identify MSOR specific barriers, find effective solutions and ultimately design inclusive curriculum delivery for the future. Good practice guideThe good practice guide necessarily draws on the particular knowledge and interests of its contributors and cannot claim to provide a comprehensive picture.Nevertheless, with contributions from different stakeholders -academic staff, professional support staff, disability researchers and students -the guide aims to be a step towards the goal of working together to develop inclusive curricula.The guide concludes with a collection of references to resources, sources of further information and key papers with short annotations.This list is provided to assist departments seeking MSOR specialist information to discover resources more effectively.Common threads that run through the contributions indicate common challenges for inclusive practice in MSOR.Contributions explore technical and pedagogic barriers and the way these may be formed by the modes in which mathematics is communicated.The contributions provide strong evidence of the need for collaboration between the MSOR community and the support professionals in dissolving these barriers and moving together towards the goal of inclusive curricula.
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 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".