Bibliographic record
Abstract
Abstract At the heart of any healthy field are explicit theories and concerted efforts to test these theories. In the traditional “textbook” conceptualization of science, the main avenue for developing and testing theory is experimental research, a tool that enables investigators to filter out the noise in order to draw logically valid inferences and conclusions. The objective of this paper is to begin a probe into the use of experimental research in knowledge management (KM). After sketching an image of the nature of experimental research and its advantages, the paper details the results of an analysis of experimental research in the KM literature. The top 20 KM journals were searched in Scopus and Web of Science for any mention of the term “experiment.” In total, 43 papers were identified based on their use of experimental methods and human participants. These studies were coded for purpose, research questions, hypotheses, operationalization of variables, sample parameters, and statistical analysis methods. There appeared to be little evidence for a dedicated and sustained use of experimental research methods. Virtually all studies relied heavily on self‐report questionnaires as the main data collection tool rather than direct behavioral measures. Potential implications are that KM journals may want to elicit and encourage more experimental research, and researchers interested in using experimental methods may want to forge multidisciplinary partnerships, for instance, with experimental psychologists. The implication for KM methodological pedagogy is to further promote and integrate experimental methods.
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 | 0.154 | 0.297 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".