Labour adjustment by employee type when sales change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We examine how companies in China manage labour resources through sales upturns and downturns. We argue that managers make implicit commitments to retain some employees through downturns based on the nature of activities the employees engage in. We predict higher commitment in contracting (more stickiness) for employees who accumulate intangible asset value and engage in other long horizon activities. We associate employees with three primary business activities: sales and marketing (S&M), accounting and financial management (A&F) and production and operations (P&O). Employees in S&M acquire product knowledge and build relations with customers that benefit the firm over time. Employees in A&F combine professional skills with knowledge of the firm to support current operations and plan for future demand. Employees in P&O apply general and firm-specific skills to service current production and sales. We discriminate between state-owned enterprises (SOEs) and non-SOEs in our analysis. For SOEs, there is stickiness in labour adjustment across all activities, consistent with political employment objectives of SOEs. For non-SOEs, firms add more employees for S&M and A&F when sales increase than they remove when sales decrease but adjustments to labour for P&O activities are symmetric with respect to increases and decreases in sales.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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 it