Experiments in Organization and Management Research
Bibliographic record
Abstract
Abstract Experiments randomly assign actors (e.g., people, groups, and organizations) to different conditions and assess the effects on a dependent variable. Random assignment allows for the control of extraneous factors and the isolation of causal effects, making experiments especially valuable for testing theorized processes. Although experiments have long remained underused in organizational theory and management research, the popularity of experimental methods has seen rapid growth in the 21st century. Gatekeepers sometimes criticize experiments for lacking generalizability, citing their artificial settings or non-representative samples. To address this criticism, a distinction is drawn between an applied research logic and a fundamental research logic. In an applied research logic, experimentalists design a study with the goal of generalizing findings to specific settings or populations. In a fundamental research logic, by contrast, experimentalists seek to design studies relevant to a theory or a fundamental mechanism rather than to specific contexts. Accordingly, the issue of generalizability does not so much boil down to whether an experiment is generalizable, but rather whether the research design matches the research logic of the study. If the goal is to test theory (i.e., a fundamental research logic), then asking the question of whether the experiment generalizes to certain settings and populations is largely irrelevant.
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 | 0.092 | 0.213 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".