Bibliographic record
Abstract
The role of leadership in science fiction receives a particular analysis which is based on what can be termed transhumanist novels published in Italy between 2008 and 2013. The main purpose of this study is to answer the following question: What happens to (the nature of) leadership in a technologically-driven society? Four novels form the backbone of the description of futuristic leadership. The four conclusions drawn from this analysis regarding the nature of leadership in a technologically-driven society point to a much greater need for leadership studies to pay attention to technological advances (and the philosophical underpinnings of, specifically, transhumanism). The impact of nano-bio-technology affecting the role of leaders, followers, goals, alignment, commitment has ontological repercussions on the manner in which (augmented and unaugmented) humans deal with each other. If early augmented humans/cyborgs and any other sentient beings are in fact comparable to Giambattista Vico’s brutes, and if his corsi e ricorsi (ebbs and flows) of human history can apply to non-human, sentient beings’ history, then the work is cut out for all disciplines, but especially for those which deal with ontologies of leadership.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.053 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".