Remuneration, Personnel Expenses and Third Party Expenses in Times of Covid-19 in the Financial Sector
Bibliographic record
Abstract
The objective of this research was to determine the statistically significant differences between the semiannual percentages of salaries, personnel expenses and third party expenses of the Peruvian financial sector during the “COVID-19” pandemic. The percentage of administrative expenses of 46 financial entities in Peru was analyzed, and in what proportion they invested in favor of their collaborators; As a result, in terms of remuneration, the Municipal Savings Banks invested in a greater proportion (50.69%), regarding personnel expenses, the Municipal Savings Banks stand out with 16.8%, and in terms of third-party payments, the financial entity that invested the most was Edpymes with 42.30%. Likewise, through the Kruskal Wallis test it was obtained that there are no statistically significant differences in remuneration, payment of personnel and payment to third parties among financial entities in Peru (p value> 0.05). It is concluded that the semester percentage variation in time of COVID-19 with respect to the first semester 2019 has had a minimal change (+ 1.20%. In salaries, -1.6% in personnel payments and -0.05% in third party expenses).
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".