Bibliographic record
Abstract
The Postdigital Learner Agency (PLĀ) framework redefines learner agency to navigate the complexities of education in the postdigital era-where hybrid learning environments and algorithmically mediated systems shape educational experiences. Grounded in Social Cognitive Theory, Sociocultural Perspectives, and Postdigital Philosophy, PLĀ extends traditional notions of agency by incorporating relational, spatial, collective, and shared dimensions. This paper explores four key questions: How must agency be redefined for hybrid realities? What theoretical foundations support PLĀ? How does it foster equity and resilience? How do sociotechnical systems influence agency? The paper then examines PLĀ's theoretical foundations and its application across K-12, higher education, and lifelong learning contexts. It demonstrates how PLĀ fosters autonomy, adaptability, and algorithmic resilience, ensuring learners are prepared to engage with complex educational and professional landscapes. By addressing systemic inequities and advocating for inclusive policies, PLĀ offers a transformative vision of education-empowering learners to act with confidence, ethical awareness, and agency in postdigital environments.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.069 |
| Scholarly communication | 0.015 | 0.019 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".