mischbeck/MiningTransparencyManuscript
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases 1 Wiki Activity

couple of tweaks, adds tables and reorders supplements.

Browse Source
This commit is contained in:
Michael Beck
2025-12-16 23:20:20 +01:00
parent ee79e207ea
commit 86b2380902
2 changed files with 92 additions and 64 deletions
Download Patch File Download Diff File Expand all files Collapse all files
index.qmd
+2 -4
View File
@@ -509,7 +509,7 @@ tbl_sample_desc <- df %>% mutate(
pvalue_fun = label_style_pvalue(digits = 3)
) %>%
add_overall() %>%
modify_header(label ~ "**Variable**") |>
modify_header(label ~ "**Variable**") %>%
modify_spanning_header(c("stat_1", "stat_2") ~ "**Statistical Inference**")%>%
modify_footnote_body(
footnote = "A: Psychology, Multidisciplinary; B: Law; C: Criminology & Penology",
@@ -782,7 +782,7 @@ if (isTRUE(debug_mode)) {
}
```
@tbl-osp-prev adjusts adjustments were applied using sensitivity and specificity from the ML-validation analysis in [@liuQuantitativeBiasAnalysis2023]. Under extreme rarity, adjustments become unstable: intervals widen dramatically (approaching $[0,1]$) or yield boundary/negative estimates when specificity is insufficient relative to prevalence. For OD, the false-positive rate ($1-\text{Sp} \approx 12.7\%$) exceeds the observed prevalence ($2.2\%$), pushing adjusted points below zero. For OM, low sensitivity ($\text{Se} = 0.20$) and tiny validation counts produce near-uninformative intervals. Given these constraints, the adjusted values can be interpreted as sensitivity ranges rather than confirmatory estimates. Any substantive claims should thereby rather be based on design-based estimates and on OA (measured from metadata).
In @tbl-osp-prev, adjustments were applied using sensitivity and specificity from the ML-validation analysis in [@liuQuantitativeBiasAnalysis2023]. Under extreme rarity, adjustments become unstable: intervals widen dramatically (approaching $[0,1]$) or yield boundary/negative estimates when specificity is insufficient relative to prevalence. For OD, the false-positive rate ($1-\text{Sp} \approx 12.7\%$) exceeds the observed prevalence ($2.2\%$), pushing adjusted points below zero. For OM, low sensitivity ($\text{Se} = 0.20$) and tiny validation counts produce near-uninformative intervals. Given these constraints, the adjusted values can be interpreted as sensitivity ranges rather than confirmatory estimates. Any substantive claims should thereby rather be based on design-based estimates and on OA (measured from metadata).
Earlier differences in text sources suggest heterogeneity by journal, thereby implicating also publisher variance [@scogginsMeasuringTransparencySocial2024]. @fig-osp-time-by-publisher visualizes OA shares over time for the 12 most prolific publishers in the sample (listed in the caption). Leveraging larger $n$, the author fit simple OLS trends to annual OA proportions. The four most prolific publishers show clear increases. Four publishers do not: Oxford University Press, Emerald, ASCE, and MDPI. MDPI remains at 100% OA, Emerald at 0% in this sample; ASCE shows an apparent decline consistent with limited observations; Oxford University Press is relatively stable. All observed increases are highly statistically significant. Future work should use models designed for proportions (e.g., binomial GLMs) and, ideally, hierarchical pooling across publishers and years.
@@ -939,8 +939,6 @@ if(output_format == "pdf/tex") {
colformat_double(
big.mark = ",", digits = 2, na_str = "N/A"
)
} else {
}