Bibliographic record
Abstract
In proposing the 2017 conference theme, "Making Sense of Humanity in a Posthumanist Age," our intention had been to mark the 30 th anniversary of Bernard Williams' 1987 Stanford Lecture, "Making Sense of Humanity."We invited authors to consider what remains of "humanity" or "the human" in a time when artificial intelligence, sophisticated robotics, and radical shifts in scientific, social, legal, and political thought have blurred the boundary between the human and non-human.When we posted the call for papers, the US presidential election had not yet happened, and most of us had no idea how urgent the question of what remains of humanity would become, as dehumanizing rhetoric became a regular feature of campaign rallies and reports on the nightly news.In the weeks leading up to our meeting, PES members from countries named in the then-newly-instated travel ban faced uncertainty about whether they would be allowed entry into the US to attend the conference or whether they would be turned away at the border.Some non-US-based members declined to cross the border as a matter of conscience, and others felt torn about whether to attend.These were challenging times on many levels, but once we came together in Seattle, the conversations were thought-provoking, invigorating, and inspiring, and technology enabled us to accommodate those presenters who could not be with us in person.Since then, we have continued to be pressed, not only intellectually, but also personally, politically, and socially, by questions of what it means to be human and how to respond to those who are most vulnerable in a way that affirms and supports their humanity.In revisiting the articles in this year's collection, it is clear that our work as philosophers of education plays a vital role in helping us begin to address the educational aspects of these questions.In this brief introduction I will not mention each article individually, but will instead speak to a few thematic threads that emerged.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.063 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".