Bibliographic record
Abstract
Big data analytics are changing product costing practice in its decision-facilitating role, and have made arbitrary overhead allocation unnecessary. Contracts-to-system applications, which extract cost data directly from contracts without resorting to conventional cost accounting, are key components of emerging practice, and are currently offered by all Big 4 accounting firms to audit and consulting clients. I call this practice contract-based cost analytics (CBCA) and illustrate it with a special order decision scenario. Benefits of CBCA are reductions in cost estimation assumptions, timeliness, intuitive appeal to non-accountants, improved access to unstructured data, improved negotiations regarding cost and sales, outsource-based budgeting, and support for capital budgeting decisions (in addition to short-term scenarios). The biggest obstacle to CBCA is accountants' familiarity with linear cost behavior assumptions. Without such assumptions, CBCA looks very unusual; the point of this paper is that albeit unusual to accountants, not only is CBCA possible, it has begun.
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 | 0.008 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".