Bibliographic record
Abstract
The writings of computer futurist writers such as Ray Kurzweil, Eric Schmidt, Hans Moravec, and Peter Diamandis argue that the digital revolution that is “Reshaping the Future of People, Nations, and Business” (to quote the sub-title of Schmidt’s book) is being driven by the Darwinian forces of natural selection. What they do not consider is how their thinking, as well as the thinking of most computer scientists and programmers, is influenced by the metaphorical language of their culture. As language both illuminates and hides, the main focus in this essay is on the diversity of cultural ways of knowing that are being lost as people rely more on print-based data, information, and other abstract systems of representation. The assumption of the computer futurist writers that the digital revolution is an inherently progressive force is based on the same metaphorical language that underlies such progress-oriented ideologies as libertarianism and market liberalism. These ideologies, in turn, are based on the Western Enlightenment assumption that traditions are the source of backwardness and a limitation on progress. This same view of traditions, which ignores that the diversity of the world’s cultural commons carried forward through face to face communication are the basis of less consumer dependent and less toxic destructive lifestyles that will become more important as the ecological crisis deepens, is a key feature of the digital revolution. The double bind is that the many important uses of digital technologies lead to the widespread indifference about the importance of living cultural traditions that are passed forward face to face and through mentoring relationships. Is the loss of privacy, communication between generations, economic security from being displaced by robots and from hackers, the diversity of cultural ways of knowing––including the exercise of ecological intelligence, to be written off in the name of progress?
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".