Bibliographic record
Abstract
egativity in the workplace can be detrimental to patient retention, not to mention your pocketbook.It only takes one negative experience for a patient to decide to try another eye doctor or optical store.There are a lot of choices out there for eyewear and eye care, so you have to ensure that you minimize any issues regarding negative behavior by your staff.Negativity typically manifests as one or two employees with attitude or authority issues.They don't like being told what to do, they don't like the way things work or they just don't like much, period.They are generally negative in all regards, and they don't stop complaining.These employees are like weeds that will keep spreading if not pulled.Not only can these malcontents affect your customers, your great employees may also eventually leave if you don't address negativity issues in your workplace.A McKinsey study concluded that 59% of employees would be "delighted" if managers dealt with problem employees.In reality, however, only 7% of employees believe that their companies are actually doing a good job in this regard.Here are 5 Tips to Minimize Negativity:
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 | 0.007 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.039 | 0.021 |
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".