Faster, smaller, cheaper: an hedonic price analysis of PDAs
Why this work is in the frame
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Bibliographic record
Abstract
We compute quality-adjusted price indexes for personal digital assistants (PDAs) for the period 1999 to 2004. Hedonic regressions indicate that prices are related to processor generation and clock speed, memory capacity, screen size and quality and the presence of a digital camera or wireless capability. A particularly salient feature of PDAs is portability, where we find: (i) purchasers value the energy density of the battery technology (e.g. lithium ion) rather than the battery life in hours; and (ii) the physical characteristics of the PDA (e.g. weight, volume) are nonlinearly related to price, suggesting that valuation of the physical form of PDAs does not bear a simple linear relationship to characteristics, either in absolute terms (‘smaller is better’) or vs. an ergonomic ‘sweet spot’. Rather, portability characteristics are correlated with other desirable attributes, making the relationship between price and portability difficult to disentangle. However, hedonic price indexes are robust across different measures of the portability of PDAs. Hedonic indexes using the dummy variable, characteristics prices, and imputation approaches decline on average between 19 and 26% per year. A matched model price index computed from a subset of observations declines at 19% per year, while a fixed-effects hedonic index declines at 14% per year.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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