Bibliographic record
Abstract
Even though shame and guilt are two widely investigated emotions, there is no consensus on what people mean when they use the terms ‘shame’ and ‘guilt’. Some researchers argue that they are indicators of focus. That is, shame means a bad self, while guilt means a bad behaviour (Tangney, 1991). However, other researchers have argued that shame and guilt must be understood in the context of ‘the self’ and ‘the other’. Since shame is more unpleasant than guilt, people that feel shame scorn themselves in addition to the scorn that derives from a condemning imagined ‘other’. This experience is so painful, that people would rather not allow shame to impact them personally; consequently they try to defend against it. One way to do this is to express guilt instead (Lewis, 1971). Building on this latter view, I will debate a new model as proposed by Gausel and Brown (2012) that argue if guilt and shame are evoked simultaneously in the face of immoral in-group behaviour, then people would allow guilt to address them personally, while shame will address their in-group. This means that guilt will motivate them to undo their personal self and their personal behaviour, while shame will motivate them to undo the in-group self and behaviour. Implications for intergroup research are discussed.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".