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
The purpose of this study is to analyze the pricing strategies of French FinTech Firms (FFFs) using quantitative descriptive and correlational research methods. Based on a representative sample of 246 FFF, the study provided consistent support for the hypotheses, which argues that FFFs with high price-setting power may implement a combination of the price-setting strategy (PSS) “skimming” and the price-setting practice (PSP) “value-informed”. FFFs applying “market-based” PSSs tend to use “competition-informed” PSP preferring “pay-per-use” price-setting model (PSM). Whilst FFFs who apply “penetration” PSS tend to use “cost-informed” PSP and “pay-per-use” PSM. The findings support founders and senior management in their pricing decisions. This paper contributes to the existing literature on pricing strategies of early-stage high-tech companies. There is a need for further research about the change of pricing strategies during the lifecycle of a firm using for example a longitudinal quantitative study.
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 | 0.008 | 0.199 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it