Policy as Boundary Object: A New Way to Look at Educational Policy Design and Implementation
Bibliographic record
Abstract
Policy implementation research in general and educational policy in particular is loosely connected to policy-making processes. There is a gap particularly in the field of vocational education. This often leads to conflicts and contradictions between policy-maker objectives and end-user implementation. To avoid such a disconnect, the relation between the world of end users and the world of decision-makers should be carefully constructed. This might be done through connecting the process of decision-making to implementation and carefully developing methods that encompass the demands of decision-makers and the needs and informational requirements of end users. Our intensive case study research on the process of a vocational education reform in the marine sector confirms that the lack of robust connection and active engagement of implementers as the end-users in the design of the policy created challenges for its implementation. Using a theoretical framework based on the notion of boundary objects we bring an alternative to current practice by creating the potential to illuminate and remove tensions and challenges for policy implementation. From there we infer that adopting this concept for policy design may prevent such conflicts and contradictions and result in successful implementation.
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 | 0.040 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.010 | 0.112 |
| Scholarly communication | 0.035 | 0.069 |
| Open science | 0.006 | 0.015 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".