The unintended consequences of information system change on organizational memory: remembering and forgetting in English and Ontarian child protection services
Bibliographic record
Abstract
This thesis set out to enhance our understanding of the unintended consequences of information technology change on organizational memory in child protection services. It sought to resolve puzzles about how ‘perfect’ digital memory could be accompanied by forgetting and how organizational amnesia could be concurrent with bureaucratic inertia. Information technology change in this sector is ostensibly aimed at addressing key challenges exposed by child death reviews, performance audits, and research into child outcomes, such as the issue of past information not being available for front-line or policy decision making. However, the promise of digitization was not always achieved. A motivation for undertaking this research was thus to better understand divergences between what was intended and what was realized. Focused and theoretically informed qualitative field research that included observation, interview, and document analysis was carried out in child protection organizations that were implementing new information technologies (the Child Protection Information Network enterprise case management system in Ontario, and predictive analytics tools using machine learning in England). Findings called into question the completeness of existing theories of organizational memory. The interpretive approach to analysis suggested that there might be additional forms of memory (grounded in fundamental ontology) that could help to account for the unexplained phenomena encountered in the field research, resolve the puzzles, and offer a novel foundation for the study of memory phenomena in government. The final contribution is an extended typology of organizational memory that can provide memory-related rationale for unintended consequences of information system change in child protection. This theoretical extension of organizational memory has implications for how we understand identity change, accountability, technology adoption, and learning in public sector organizations. The findings suggest that if information technology is built without attending to the different forms of memory at play, then it may not achieve its desired objectives.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".