Bibliographic record
Abstract
The enhancement of human capability ushers in a new age of technology as people call for machinery to take over their work, increase human efficiency, and make the impossible become possible. But the major challenge organizations experience is the high level of resistance towards adoption of such innovations into the firm's operations. This study aims at discussing the dynamic factors behind technological resistance and how the necessary culture can be built. Some of these factors are perceived job loss, lack of knowledge on the new technology such as AI and biotechnology, culture and age differences in adoption of technology, ethics such as privacy and autonomy, bureaucratic resistance, psychological barrier to change, lack of resources, resistance to technology-based performance indicators, and fear of obsolescence of skills. Thus, there is a need to focus on such solutions as clear reporting by organizations, development of specific programs for effective training, ethical standards within the frameworks of the organization, considering employees, their views, ideas, and promoting widespread commitment and leadership from managers for stimulating innovation. Thus, proper management of change in organizations can prevent resistance and bring people and technologies to improve effectiveness, innovation, and strategic positioning in the age of high-tech environments. As for practical applications, this study calls for a partnership and future-oriented approach towards unveiling and addressing the challenges of technological implementation, and in turn, outlining the opportunities of human enhancement technologies to organizations as well as the society and ethics.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".