Reconstructing Human Value in the Age of <scp>AI</scp> : From Replacement to Liberation?
Bibliographic record
Abstract
ABSTRACT The rapid development of artificial intelligence (AI) has sparked growing concern over its potential to replace human workers across various industries. While fears of job displacement is valid, AI presents a dual‐edged sword: it not only threatens traditional forms of work but also opens up new opportunities for human liberation—understood here as freeing individuals from repetitive, monotonous tasks and enabling them to focus on more creative, fulfilling, and human‐centric work. This shift presents an opportunity to reconsider and redefine the value of human existence in the context of advanced technology. This panel brings together experts to explore how AI might evolve from a tool of displacement into one of liberation, and how we might reconstruct the value of human existence beyond work. The discussion draws on interdisciplinary perspectives and is developed in dialogue with academic communities such as ASIS&T, aiming to balance AI's potential with the preservation of human creativity, identity, and dignity—ensuring that AI advancements foster human empowerment and contribute positively to the societal fabric.
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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.088 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".