Bibliographic record
Abstract
It seems more and more students feel "entitled". "Entitlementality" in action looks like sitting around, lacking accountability, expecting success to come without effort or work, and surprised and offended when things don't work out perfectly. It looks like students who expect teachers to do a high level of their work and their thinking FOR them. Meanwhile, borders are opening, careers are ever-changing, and economies are shaky. An attitude of entitlement in this age is a recipe for failure. Educators MUST combat this "entitlementality." Society, although well-intentioned, has unfortunately caused the problem. A relatively "good" and easy life, disconnectedness from nature and other real systems, and a mangled message of "empowerment" decoupled from hard work have symbolically changed L'Oreal's slogan from "I'm worth it" into a whiny "I deserve it." This is a battle worth fighting, and the paper offers techniques to do it – good goals in and of themselves: creativity, empathy, gratitude, and meaningful personal projects.
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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.038 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.004 | 0.018 |
| 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".