Bibliographic record
Abstract
A review of the Information Systems literature reveals that experiments are employed infrequently by IS researchers. Further, there is some evidence that the use of experiments is not increasing and may, in fact, be declining. A relatively new approach, the Web experiment, is introduced as a tool for Information Systems researchers to take advantage of the benefits of experimental methods, while avoiding some of the costs. A Web experiment resides on a Web site and can be accessed online through a browser. The experiment includes one or more independent variables that are dynamically manipulated through the Web. Subjects are typically remote from the experimenter and are free to complete the experiment at a time and place of their choosing. The strengths and weaknesses of Web experiments are explored.
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 | 0.084 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.066 |
| Scholarly communication | 0.018 | 0.041 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.010 | 0.026 |
| Insufficient payload (model declined to judge) | 0.020 | 0.007 |
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".