Comparative Analysis of Policy and Practice of Kazakhstan's Open Data
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
The article presents an analysis of Kazakhstan’s open data policy and portal, comparing them with those of the European Union, Canada, and Estonia. It highlights the importance of both core policies and implementation measures, as well as the supporting ecosystems composed of various actors both inside and outside government. The article also includes results from a limited practical and technical evaluation of selected open data portals, focusing on usability, data functionality, and accessibility, along with use cases and directions for open data implementation. While specific proposals for improving the implementation of Kazakhstan’s open data policy are provided – together with relevant use cases and implementation directions – we hope the findings will also be of interest to readers concerned with the open data policies and portals of Canada, Estonia, and the EU. Finally, the article offers an overview of insights gathered during the research conducted in November 2022, supported by surveys of civil servants from both local and central government bodies with engagements taking place in January 2023. The survey data provides valuable information about participants’ innovative ideas for using open data to address current challenges in the country, as well as their forward-looking attitudes toward future reforms. Keywords: Open Data, Open Data Portal, Information, Proactive Dissemination of Information, Socially Significant Information, Personal Data.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Open science Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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