Bibliographic record
Abstract
A growing body of knowledge is being accumulated in the area of global information management (GIM). Research in this area has grown significantly in the 1990s. Not only are established IS journals publishing an increasing amount in this area, but there are now specific journals devoted to the major issues in the development, use, and management of global information systems. However, much of this research has been limited to isolated survey studies or case studies into particular aspects of GIM. This has resulted in a rather disjointed and ad hoc development of this literature that now needs some structure to further its development. The purpose of this chapter is to provide a framework for research into GIM. It hopes to set a future direction for research in this area by challenging IS researchers to consider studying a number of potentially productive subareas of GIM that the framework has identified as being unstudied or understudied. This research framework builds on the general IS framework of Ives, Hamilton, and Davis (1980) and surveys the GIM published literature between 1990 and 2000. The application of this literature to the Ives, Hamilton, and Davis framework indicates where much GIM research has been conducted and where further research needs to be done.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.009 |
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".