Bibliographic record
Abstract
Although there are numerous studies and discussions about the concept of value, it is claimed to lack an objective basis. The concept of value is difficult to define from a single perspective because it relates to many disciplines, especially the social sciences. Philosophy, sociology, psychology, religious sciences, anthropology, and historical sciences have attributed different meanings to the concept of value. However, the fact that the concept of value is related to human behavior has been a common point in all disciplines of social sciences. Values are essential in terms of social sciences because they interpret human behavior (Ulusoy & Dilmaç, 2016). Therefore, this study aimed to determine what academics thought about the core values lost in society and what kind of solutions they offered to that problem. The sample consisted of 16 academics from Van Yuzuncu Yıl University/Turkey. The sample consisted of 12 men and 4 women. The study adopted a qualitative research design. Data were collected using a semi-structured interview questionnaire consisting of open-ended questions. The data were analyzed using descriptive analysis. The findings focused on participants’ views of the concept of values, the values lost in society, values problems, and solutions to the lost values.
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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.019 | 0.048 |
| Scholarly communication | 0.022 | 0.014 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".