Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Beginning with Bowersox and Daugherty's (1987) influential work describing three unique logistics organizational forms, researchers have generally taken a theoretical typology approach to classifying logistics strategies, and attempts to validate the numerous proposed typologies have produced inconsistent and somewhat conflicting results. In an attempt to add clarity to this stream of research, the current article partially replicates and extends the previous studies using a more rigorous and data‐driven methodology, by developing an empirical taxonomy with firmlevel logistics activities used as clustering criteria. The results identify two primary logistics strategy types used by contemporary firms. The revealed strategies are somewhat parallel to two of the three strategic orientations proposed within the original Bowersox and Daugherty (1987) typology, but also elements suggested by other researchers, as well as new concepts introduced since the original work was published. Based on the results, implications of the revealed logistics strategy taxonomy are provided for managers, and foundations are laid for researchers seeking to undertake further inquiry in the area.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it