restore original section bibliographies filter

_extensions/pandoc-ext/section-bibliographies/section-bibliographies.lua

@@ -20,7 +20,7 @@ local orig_bibliography
 local function is_section_div (div)
   return div.t == 'Div'
     and div.classes[1] == 'section'
-    -- and div.attributes.number
+    and div.attributes.number
 end
 
 local function section_header (div)
@@ -44,13 +44,11 @@ local function adjust_refs_components (div)
       return b.attr and b.identifier == 'refs'
   end)
   if bib_header then
-    -- bib_header.identifier = 'bibliography-' .. header.attributes.number
-    bib_header.identifier = 'bibliography-' .. (header.attributes.number or 999)
+    bib_header.identifier = 'bibliography-' .. header.attributes.number
     bib_header.level = header.level + 1
   end
   if refs and refs.identifier == 'refs' then
-    -- refs.identifier = 'refs-' .. header.attributes.number
-    refs.identifier = 'refs-' .. (header.attributes.number or 999)
+    refs.identifier = 'refs-' .. header.attributes.number
   end
   return div
 end
@@ -146,6 +144,5 @@ return {
   remove_pandoc_citeproc_results,
   {Pandoc = setup_document},
   {Div = create_section_bibliography},
-  {Meta = restore_bibliography}
-  -- {Div = flatten_sections, Meta = restore_bibliography}
+  {Div = flatten_sections, Meta = restore_bibliography}
 }

_quarto.yml

@@ -27,9 +27,9 @@ format:
     documentclass: scrreprt
     keep-tex: true
     include-in-header: resources/tex/preamble.tex
-    # citeproc: false # don't run pandoc default citation handling
-    # filters:
-    #   - section-bibliographies
+    citeproc: false # don't run pandoc default citation handling
+    filters:
+      - section-bibliographies
     toc-depth: 1 # level 1 headings (i.e. parts and chapters)
     toc-title: Contents # title of table of contents
     links-as-notes: true # turn URLs into sidenotes

index.qmd

@@ -26,7 +26,7 @@ At the end of the course, we reap the harvest of these projects. The project pre
 knitr::include_graphics("images/dog.jpeg")
 ```
 
-This book is an attempt to distill some of the lessons of the course (and students' course projects) into a textbook. We'll tell the story of the major shifts in psychology that have come about in the last ten years, including both the "replication crisis" [@osc2015 et seq.] and the positive methodological reforms that have resulted from it. Using this story as motivation, we will highlight the importance of transparency during all aspects of the experimental process from planning to dissemination of materials, data, and code. 
+This book is an attempt to distill some of the lessons of the course (and students' course projects) into a textbook. We'll tell the story of the major shifts in psychology that have come about in the last ten years, including both the "replication crisis" and the positive methodological reforms that have resulted from it. Using this story as motivation, we will highlight the importance of transparency during all aspects of the experimental process from planning to dissemination of materials, data, and code. 
 
 ## What this book is and isn't about {-}
 
@@ -63,9 +63,9 @@ While case studies are often integral to the chapters, the other boxes can typic
 We highlight four major cross-cutting themes for the book: **transparency**, **precision**, **bias reduction**, and **generalizability**. 
 
 * **Transparency**: For experiments to be reproducible, other researchers need to be able to determine exactly what you did. Thus, every stage of the research process should be guided by a primary concern for transparency. For example, preregistration creates transparency into the researcher’s evolving expectations and thought processes; releasing open materials and analysis scripts creates transparency into the details of the procedure. 
-* **Precision**: We want researchers to start planning an experiment by thinking “what causal effect do I want to measure” and to make planning, sampling, design, and analytic choices that maximize the precision of this measurement. A downstream consequence of this mindset is that we move away from a focus on dichotomized inferences about statistical significance and towards analytic and meta-analytic models that focus on continuous effect sizes and confidence intervals [@cumming2014].
+* **Precision**: We want researchers to start planning an experiment by thinking "what causal effect do I want to measure" and to make planning, sampling, design, and analytic choices that maximize the precision of this measurement. A downstream consequence of this mindset is that we move away from a focus on dichotomized inferences about statistical significance and towards analytic and meta-analytic models that focus on continuous effect sizes and confidence intervals.
 * **Bias reduction**: While precision refers to random error in a measurement, measurements also have systematic sources of error that bias them away from the true quantity. In our samples, analyses, experimental designs, and in the literature, we need to think carefully about sources of bias in the quantity being estimated. 
-* **Generalizability**: Complex behaviors are rarely universal across all settings and populations, and any given experiment can only hope to cover a small slice of the possible conditions where a behavior of interest takes place [@yarkoni2020]. Psychologists must therefore consider the generalizability of their findings at every stage of the process, from stimulus selection and sampling procedures, to analytic methods and reporting.
+* **Generalizability**: Complex behaviors are rarely universal across all settings and populations, and any given experiment can only hope to cover a small slice of the possible conditions where a behavior of interest takes place. Psychologists must therefore consider the generalizability of their findings at every stage of the process, from stimulus selection and sampling procedures, to analytic methods and reporting.
 
 Throughout the book, we will return to the important relationships between these four concepts, and how the decisions made by the experimenter at every stage of design, data gathering, and analysis bear on the inferences that can be made about the results.