Bibliographic record
Abstract
It was in 1996 that Integra1, a large Canadian life insurance institution, launched its Banking and Loan Insurance Software System (BLISS) development project with the aim of gaining access to the loan insurance market in small Credit Unions (CUs) across Canada. The company was ready to provide the system free of charge to the Credit Unions on the provision that they commercialize exclusively Integras loan insurance products. To achieve this goal, Integra entered into a partnership with Intex Consulting, the Canadian subsidiary of a large international information system (IS) integration firm who wanted to gain a foothold in the Canadian banking business. After 1.3 million dollars of investment from each partner and twelve months of intensive efforts, the project came to an abrupt stop. The lessons learned in this case study include: (1) the importance of understanding requirements beyond micro-level user needs, (2) the need to get the enlightened involvement of each interested party in a large complex project, (3) the importance of appraising the specific contribution of each partner in a strategic alliance, and (4) the obstacles faced when entering an unfamiliar market with a new, unproven IS product.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.012 |
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".