Considerations of Self in Recognising Prior Learning and Credentialing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Discussions about recognition of prior learning (RPL) and credentialing frequently focus on issues of equivalency and rigour, rather than the effects of assessment on self-structure. Yet, such processes invite reflexive self-assessment that results in either a conformational or destabilising effect on self-identity. Those interested in RPL therefore need to understand how the process impacts on self and how learner needs associated with those impacts may be met. This chapter explores the self as a sub-text within the RPL process and argues that learners should be viewed as holistic and complex beings and that educational strategies can meet multiple objectives that extend beyond the educational domain, potentially creating an overlap with learners' mental health. The authors encourage policies and practices that validate the individual and enhance the possibility of developmental self-growth. A learner-centred ethic that meets the dual needs of learners to obtain credit and achieve self-development is proposed.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it