Bibliographic record
Abstract
Information technology outsourcing—the practice of transferring IT assets, leases, staff, and management responsibility for delivery of services from internal IT functions to third party vendors—has become an undeniable trend ever since Kodak’s 1989 landmark decision. In recent years, private and public sector organizations worldwide have outsourced significant portions of their IT functions, among them British Aerospace, British Petroleum, Canadian Post Office, Chase Manhattan Bank, Continental Airlines, Continental Bank, First City, General Dynamics, Inland Revenue, JP Morgan, Kodak, Lufthansa, McDonnell Douglas, South Australian Government, Swiss Bank, Xerox, and Commonwealth Bank of Australia (Hirsheim & Lacity, 2000). How should firms organize their enterprise-wide activities related to the acquisition, deployment, and management of information technology? During the 1980s, IT professionals devoted considerable attention to this issue, primarily debating the virtues of centralized, decentralized, and federal modes of governance. Throughout the 1980s and 1990s, IT researchers anticipated and followed these debates, eventually reaching considerable consensus regarding the influence of different contingency factors on an enterprise’s choice of a particular governance mode (Sambamurthy & Zmud, 2000).
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.023 |
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".