Identifying historical policy regimes in the Canadian and Australian communications industries using a model of path dependent, punctuated equilibrium
Bibliographic record
Abstract
Comparative policy analyses can be enriched by systematically examining temporal sequences over long periods of time. Yet the literature provides little guidance on operationalizing a systematic approach to trace how “history matters”. In this article, we introduce a model of path dependent, punctuated equilibrium to demonstrate how technological and institutional legacies restrict the policy options available for deploying new communications technologies in Canada and Australia. The research adopts a long-term view of the respective communications industries beginning with the policy choices made from the time of the telegraph and the resulting policy paradigms that continue to influence policy choices made in the present. We find that the consistency of these approaches can be explained by the concepts of technological momentum and policy regimes that reinforce the original policy rationale adopted to deploy the telegraph. Many other types of networked infrastructure exhibit similar characteristics of path dependent, punctuated equilibrium in that it is difficult to undo the legacies, including the sunk-costs, interests, and habits that form around the use of the respective networks and their related institutions. We posit that the model presented here will prove useful in tracing networked infrastructure policies over time, particularly in comparing cross-national policy approaches.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".