Cross‐Domain Information Integration in Government: Hierarchies and Responsibilities
Bibliographic record
Abstract
ABSTRACT Cross‐domain integration of information is increasingly identified as a priority across public sector contexts because (in theory) it enables the use of more information, including information from groups and communities historically excluded from public sector decision making. In this paper, we reject the tendency to take ‘integration’ for granted, arguing the need to position cross‐domain integration as an information practice, and conducting mixed methods thematic analysis of government strategic documents to validate the utility of this approach. We find that depending on the type of information proposed to be integrated — digital data versus the knowledge of peoples and communities — our sample of Canadian government institutions treats cross‐domain integration with differing levels of procedural rigour and detail. Reflecting ASIS&T 2024 themes of prioritizing responsibility and reflexivity in information practice, and of cultivating community partnerships through practice, not merely in name, we discuss the information hierarchies that emerge in the cross‐domain information integration in government and the associated impacts on stakeholder communities.
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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 | 0.056 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.016 | 0.044 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".