Toward an Integrated Model for Public Technology Policy Analysis - a Taxonomy Useful for Determining Scope and Type of Analysis
Bibliographic record
Abstract
How might the analysis of public technology policy be further systematised and made more objective so that important factors such as conscious subjects, interests, infl and prioritisation could better be taken into account by policy analysts and decision-makers? An integrated model designed to guide and structure the analysis process might comprise a solution to this problem. An adequately sophisticated yet concise and systematic framework of this sort would necessarily take into account the referential point of departure and alternative types of policy analysis as well as the nature, interests and priorities of those entities actually shaping public technology policy. The segment of the proposed integrated model and which is concerned with built-in assumptions about the nature of technological development has been covered in a previous article [1]. The purpose of the current article is to develop and present one further part or segment of an integrated model for public technology policy analysis (IMTPA) and to demonstrate its methodological and analytical utility with central policy analysis documents from Canada during the period 1990-2005. This article shall limit itself to a part of the IMTPA concerned with the type and scope of public technology policy analysis to be undertaken - a methodology which might better guide and make more transparent both the policies being examined as well as the policy analysis process itself.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".