Reflection on “Ways of Seeing the Recognition of Prior Learning: What Contribution Can Such Practices Make to Social Inclusion?”
Bibliographic record
Abstract
This reflective piece tracks the origins and evolution of Judy Harris’s work over 15 years. The original “Ways of Seeing ...” (Harris, 1999) provided an unsentimental reading of the social and political functions of recognition of prior learning (RPL) practices. Her premise was, as it still is, that RPL has few intrinsic characteristics of its own – it can take many forms and contribute to a variety of social ends, not all of which are as “progressive” or “radical” as advocates believe them to be. Drawing mainly from the sociology of education, Harris has pushed for understandings of RPL that take into account different types of knowledge that embrace pedagogy as well as learning, and that are based on realistic readings of the possibility for and desirability of change in particular contexts at particular social moments. Comparisons are drawn between RPL and the more recent open educational resources (OERs) and massive open online courses (MOOCs), arguing that they all raise similar sociological and pedagogical questions that may render them less democratic and equitable than they appear at first sight. To view the original “Ways of Seeing ...” article, please see the Resources section of this issue of PLA Inside Out .
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.052 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".