Bibliographic record
Abstract
Introduction -Using human or animal material for teaching histology raises ethical questions about sourcing, handling, distribution, accessibility, use and ultimate disposition.We propose an ethical audit approach as a framework for decision-making.Ethical audits were developed for supply chains, to address issues such as exploitative labour practices and environmental impacts of manufacturing.The need for an ethical framework for histology teaching is needed to assess existing collections of slides, as well as how we digitise and distribute them.With many institutions relying on online delivery for teaching materials, and resource sharing increasing, digital histology images can be distributed and used globally.In addition to ethical considerations of dignity, consent and confidentiality, histology teaching material raises concerns about intellectual property and copyright.Objective -Using the concept of the supply chain to develop an ethical audit process for using human and animal material in histology teaching.Materials and Methods -We reviewed business ethics literature to develop a conceptual framework for the creation, distribution, use and disposition of histology slides and derived digital images.Results -Using the ethical audit approach, we have identified the following steps for consideration: identifying a need and purpose for histological material in teaching; sourcing material, whether human or animal; manufacturing via tissue processing and digital image creation; recovery of production costs; copyright and intellectual property rights, and custodianship of material; making material available for use; storage of physical and digital material; who uses material and how; future disposition (storage and/or disposal); and At each stage, ethical issues can be assessed, such as minimising harms to people, animals and the environment, and weighing harms against potential benefits.Legal issues of consent, privacy and confidentiality can also be considered.Conclusions -Whilst usually applied in commercial settings, considering the production of histology slides as a supply chain is useful for assessing ethical concerns in their use in teaching.This framework can assist institutions within their local legal and sociocultural contexts to address disposition of existing glass slide collections, when and how to generate new materials, and the digitisation and distribution of digital images.Significance & future directions -Using ethical audit of supply chains provides a framework for considering ethical aspects of using human and animal materials.This can be made explicit in our teaching with our students.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.023 |
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; both teacher heads agree on what is shown here.
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".