Bibliographic record
Abstract
In general, turnover in accounting staff seems to be par for the course. In fact, it’s not uncommon for a typical accounting department to lose a quarter of its staff in a given year. With the average cost of turnover calculated at $32,500 per employee, not only is it expensive but is highly disruptive to an organization. It’s the monthend close process where the impact of a high employee churn rate is frequently most felt. This is because for most organizations, the close process continues to be a highly manual one that typically is not well documented. When an employee leaves, they often take their “tribal knowledge” of a company’s close process with them, leaving the rest of the team to scramble to reconstruct it. This can delay close time and lead to mistakes. Close management software is helping accounting departments deal with the reality of the high turnover of staff accountants. By documenting all procedures in a centralized place and enforcing best practices as well as easy access to historical referencing, it serves as an on boarding roadmap for employees in new roles and keeps the close process where it belongs-within the walls of a given organization. © 2017 Wiley Periodicals, Inc.
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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 | 0.012 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 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".