Proceedings from an International Conference on Computers and Philosophy, i-C&P 2006 held 3-5 May 2006 in Laval, France
Bibliographic record
Abstract
The increasing interaction between Philosophy and Computer Science over the past 40 years has lead to many position taking stances in theories of mind, applied machine-embedded intelligence and cultural adaptations to the onslaught of robots in society. This volume constitutes a key contribution to the body of knowledge within or about the intersection of the two fields. Is there a proper answer to the question of whether machines can think? Contemporary thought on computers and Artificial Intelligence is not the exclusive aim of the project; the birth of original forms of machine intelligence can inform us about potential human beliefs and permissibility thresholds with regards to technology i.e. are all communities equally footed with respect to machines that speak? The texts in the present set of proceedings contain the full-length versions of the papers presented at the i-C&P conference. EOARD, along with local authorities, financed this scientific event bringing together specialists in the study of minds and machines from twenty-two countries. Nearly all the papers were presented in English. The texts are divided into four sections. The preliminary section contains Keynote Addresses including the Paul Ricoeur Lecture given by Francis Jacques (in French). Section 1 contains the more technical papers, some of which are oriented towards studies in communication. Section 2 contains solid work delving into slightly less scientific notions in order to bring the notions of the mind, cognitive science and values into discussion. The texts in Section 3 take on the logical basis of mind, conceptual relations as well as interrogative techniques. The harmony of the authors work can be underlined thanks to a general focus on the notion of thought, hence the titles of the sections proposed.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.151 | 0.037 |
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".