Identifying constraints in the value chain of knowledge based services in Canada: case study of Canadian accounting firms
Bibliographic record
Abstract
With the digital revolution and dramatic fall in the international telecommunication costs, the world has become ' flat'--the leveling of playing field between developed and developing countries. This study was cast in the background of increasing trend globally among firms in manufacturing and service sector areas to fragment their production across firms and geographical locations. In Canada too, substantial work in the manufacturing sector as well as service sector especially in knowledge based areas like customer support, information systems, software programming, accounting, medical technicians etc., is outsourced to other firms/countries. The present study examines the prospects of value addition of a knowledge based industry--accounting industry in Canada. The study uses accounting firms in Prince George, BC, as a case study. Accounting firms in Prince George represent small, medium and large firms and as such represents a cross-section of accounting firms in Canada. Our investigation found that there is considerable constraints in the form of skill shortages, timeliness in delivery of accounts, employee retention and other cost overrun issues among accounting firms in Prince George, British Columbia.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".