Who owns us in perpetuity?: A question of intellectual property, copyright, and information policy
Bibliographic record
Abstract
In January of 2021, Aaron Ansuini, a student at Concordia University (Canada) posted the following tweet: “HI EXCUSE ME, I just found out the prof for this online course I’m taking *died in 2019* and he’s technically still giving classes since he’s *literally my prof for this course* and I’m learning from lectures recorded before his passing ..........it’s a great class but WHAT” The instructor, François-Marc Gagnon passed away in 2019. Tamara Kneese, writing for Slate, homed in on the key issues at stake: “This case may be particularly egregious, but it intersects with larger questions about copyright and control over faculty members’ online course materials and the various ways faculty labor within higher education is degraded and devalued,” all of which have become sharper and more important given the en masse move to online education during COVID-19. There are norms, ethics, and policies at stake concerning the creation, use, and distribution of online course designs and learning objects. A central issue is that online courses are often embedded in institutionally managed learning management systems, such as Canvas and Blackboard, and other educational technologies. All of the digital artifacts, then, are able to be duplicated, remixed, shared and reused - with or without the original instructor’s knowledge or express permission. The result is that online instructors are more susceptible to having their intellectual labor and property exploited by their institutions to serve administrative and financial interests. The American Association of University Professors’ (AAUP) Statement on Online and Distance Education states that: The institution should establish policies and procedures to protect its educational objectives and the interests of both those who create new material and those who adapt material from traditional courses for use in distance education. The administration should publish these policies and procedures and distribute them, along with requisite information about copyright law, to all concerned persons [….] Provision should also be made for the original teacher[/]creator, the teacher[/]adapter, or an appropriate faculty body to exercise control over the future use and distribution of recorded instructional material and to determine whether the material should be revised or withdrawn from use. Sponsored by the Information Policy special interest group (SIG), this panel will be composed of three speakers that will deliver presentations that address the intersection of policy and ethics regarding online instructors’ intellectual property, with special emphasis on AAUP’s point that online instructors should be able to control their use of their course designs and artifacts. Kip Currier will provide an overview of works made for hire practices and trends in academe, as well as suggest some strategies and best practices for equipping faculty to better understand the works made for hire suite of boiler plate requirements and restrictions, as well as opportunities for leveling the IP ownership field. Suliman Hawamdeh will speak about the need to update guidelines to protect the interests of the organization. And, Emily Knox will weigh in as the former interim Associate Dean of Academic Affairs regarding institutional issues relevant to these policies.
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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 | 0.032 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.031 | 0.106 |
| Scholarly communication | 0.048 | 0.091 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.029 | 0.019 |
| Insufficient payload (model declined to judge) | 0.012 | 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".