Patio Swings Intermodal Shipping Competition: An Inquiry‐Based Partial Information Exercise
Bibliographic record
Abstract
ABSTRACT Rather than providing all the required information as classroom exercises typically do, this international purchase and intermodal transportation competitive in‐class exercise intentionally holds back selected supply chain details. This inquiry‐based learning (IBL) approach simulates a real‐world Distribution Requirements Planning scenario by requiring students to identify what information they need and seek out those details from the instructor while competing with fellow student groups. In this 20‐30 minute exercise students are challenged to identify the all the necessary supply chain activities required to effectively ship patio swings from a supplier in China to a national retail chain in time for a spring sale. Generating the benefits of improved critical thinking in a fraction of the time required for traditional IBL, the approach is best described as a Partial Information Exercise. A student survey (n = 310) found that students strongly supported the inquiry approach, it generated significantly increased interest in global supply chain management roles and responsibilities, and over 91% of participants recommended the exercise continue to be part of the introductory operations and supply chain management course.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".