Bibliographic record
Abstract
Logistics is regarded as an area of high importance in business, contributing to profitability and competitiveness.Logistics is crucial also from a societal perspective, since logistical activities count for a big proportion of a country's GNP, and since effective logistics systems can help reducing some of the environmental problems we face today.Higher education has an important role to play in order to provide business and society with well-educated logistics personnel.Since not much research is published within higher education in logistics, the purpose of this thesis was defined as:To contribute to the knowledge on teaching and learning logistics in higher education.More specifically, two research questions were set up:What knowledge and skills are important for students to learn during higher education in logistics?RQ2: How can students' learning of these skills and knowledge be facilitated?A comprehensive literature review serves as a basis for the study.The literature on logistics education gives limited guidance concerning what is to be learned during higher education in logistics, as well as how to facilitate learning within logistics.These findings indicate that the logistics teaching faculty do not base their course designs and teaching practices on solid knowledge on what and how to teach.Although a major finding of my work is that more research is needed, some more concrete propositions can be made.In order to reach some kind of answers to the research questions, a selection of pedagogical theories was applied on logistics education with help from illustrating examples, partly found in literature, and partly from specific studies performed as part of this thesis.Concerning the first research question, I propose a tentative model, illustrating how different logistics knowledge and skills can be positioned against each other.A division is made between subject-specific and generic knowledge and skills, and two core generic skills within logistics are proposed: Total cost analysis and Structured investigation method.From pedagogical literature, the concept of thresholds was introduced.A threshold refers to something that is troublesome for students to overcome, but once passed leads to a new way of understanding.The identification of the thresholds associated with acquiring important knowledge and skills, is therefore important for teachers.Some thresholds concerning logistics education are discussed in the thesis.For the two core generic skills proposed above, it is suggested that 'case-specific adaptation of total cost models' is a threshold for total cost analysis, and 'investigation planning' is suggested as a threshold for structured investigation method.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".