The World IT Project: History, Trials, Tribulations, Lessons, and Recommendations
Bibliographic record
Abstract
We conceived The World IT Project, the largest study of its kind in the IS field, more than a decade ago. This ambitious mega project with an enormous global scale was formally launched in 2013 and is expected to finish by 2017. Major publications on the project should appear through 2019. The project responded to the pervasive bias in IS research towards American and Western views. What IS research glaringly lacks is a global view that tries to understand the major IS issues in the world in the context of unique cultural, economic, political, religious, and societal environments. The World IT Project captures the organizational, technological, and individual issues of IT employees across the world and relates them to cultural and organizational factors. This first major paper provides the project’s objectives and history, its general framework, governance, important decision points, and recommendations for future researchers based on lessons learned. Ultimately, we hope to provide a world view of IT issues that will be relevant to stakeholders at the firm, national, and international levels. We also invite scholars to send their recommendations for analyzing and writing papers using our vast database.
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 | 0.184 | 0.306 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".