Work the five delivered sources into the pages

The source-request loop paid for itself immediately — two of the five
corrected something rather than confirming it.

The Economist piece settles V1, the riskiest live claim, but our note
had over-claimed: truncating the scale is its *first* example, not one
of "several." Note rewritten to quote her. The same article turned out
to contain a dual-axis chart she caught herself on, now cited.

Datawrapper's color-scale part 1 refuted the assumption behind a queued
sin outright: it makes no argument against rainbow scales and endorses
multi-hue sequential gradients. The rainbow entry is now marked as
having no verified source at all, rather than ample backing.

The dual-axis post independently draws the same line we drew last turn
— of four uses only the alternative-scale case survives, their example
being F against C — and led to two references worth more than the blog
post: the Isenberg et al. study that tested dual-scale charts, and Few's
article working through the cases. Both ship without full metadata
rather than guessed metadata; queued.

Pie and stacked posts gave verbatim support, plus one correction: a
100%-stacked chart has two readable baselines, not one.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01ULE5RRxdQE1ebwefEd2eCM

docs/sources/datawrapper-color-scales.md

@@ -0,0 +1,41 @@
+# Datawrapper — "Which color scale to use when visualizing data" (part 1 of 4)
+
+Lisa Charlotte Muth, `blog.datawrapper.de/which-color-scale-to-use-in-data-vis/`,
+**16 March 2021**. Supplied as PDF, read 2026-08-05. Partly answers queue row 3.
+
+## This does not say what I assumed it said
+
+I had queued this expecting it to condemn rainbow/spectral scales, and wrote in
+the backlog that the series was "the best free treatment of this anywhere" for
+that purpose. **Part 1 makes no such argument.** It is a taxonomy — categorical
+vs sequential vs diverging, classed vs unclassed — and its closing section is
+called "It's not as clear-cut as it seems."
+
+Worse for my assumption, it actively softens the single-hue rule:
+
+> You can use only one hue in your sequential gradients (e.g., light blue to
+> dark blue) – but almost all examples I show here use multiple hues (e.g.,
+> light yellow to dark blue). Using two or even more hues increases the color
+> contrast between segments of your gradient, making it easier for readers to
+> distinguish between them.
+
+So "sequential must be one hue" is **not** a rule we can attribute here, and a
+future rainbow sin must not be written as though multi-hue gradients are the
+problem. The problem with rainbow scales is non-monotonic lightness and implied
+banding, which is a different claim needing a different source.
+
+## What it is good for
+
+- Clean definitions of categorical / sequential / diverging, and classed vs
+  unclassed, if a future sin needs to explain the vocabulary.
+- The framing that a color scale is a *mapping* to data, same as any axis.
+- Its own reference list points at Robert Simmon's *Subtleties of Color* (2013)
+  and Wilke's color-scales chapter — both plausible homes for the actual
+  rainbow argument.
+
+## Still needed for the rainbow sin
+
+Parts 2, 3 and 4 of this series, and Kosara's "How The Rainbow Color Map
+Misleads" (referenced by search results, never read). Re-queued. Until one of
+those is in hand, the rainbow sin has **no** verified source and should not be
+drafted.

docs/sources/datawrapper-dual-axis.md

@@ -0,0 +1,70 @@
+# Datawrapper — "Why not to use two axes, and what to use instead"
+
+Lisa Charlotte Muth, `blog.datawrapper.de/dualaxis/`, published **8 May 2018**,
+intro updated **July 2026**. Supplied as PDF, read 2026-08-05.
+Answers queue rows 2 and (partly) 1.
+
+## The July 2026 revision, verbatim
+
+> We originally published this article in May 2018 to explain why you couldn't
+> create dual-axis charts in Datawrapper. Since then, we've changed our minds.
+> We've learned that in some cases, dual-axis charts really are the best way to
+> show the data — and that people who've learned to read them correctly (in
+> financial services, for example) aren't misled by them. […] But all the issues
+> this original article pointed out are still valid. A general audience is
+> indeed likely to misread dual-axis charts.
+
+So: audience-dependent, not a reversal. They now ship the feature on a Business
+plan and kept the article standing. They also link a newer piece, "What to
+consider when creating dual-axis charts" (23 July 2026), not supplied here.
+
+## They draw the same line we drew
+
+The article lists four reasons people reach for dual axes, then says:
+
+> of these four use cases, we think that only the last dual axis chart can be
+> used without being potentially misleading, since it only uses the second
+> Y-axis to show an alternative scale and not a second data series.
+
+The fourth case is **Fahrenheit and Celsius on one series** — the same example
+our sin page arrived at independently. Their test and ours match: the sin is a
+second *data series*, not a second *scale*.
+
+## The three problems
+
+1. **"Zero baselines at different heights can mislead."** The proportions are
+   arbitrary. Their worked example: German vs global GDP looks like it rises at
+   the same rate; extended to zero, global rose 80% and Germany 40%.
+2. **"Even zero baselines at the same height can mislead"** — "humans have a
+   tendency to set things in relation if they're close-by." Readers conclude
+   German GDP exceeded global GDP, then crossed in 2011.
+3. **"They're just hard to read."**
+
+Nutshell line: *"The scales of dual axis charts are arbitrary and can therefore
+(deliberately) mislead readers about the relationship between the two data
+series."*
+
+## The empirical source behind it — worth citing directly
+
+Isenberg, Bezerianos, Dragicevic & Fekete (2011), *A Study on Dual-Scale Data
+Charts*. 15 participants, four chart types; the dual-axis ("superimposed")
+chart was quoted as:
+
+> We found across the board that the superimposed chart performed poorly both
+> in terms of accuracy and time. […] it was ranked lowest by all but one
+> participant. Participants called it very confusing and demanding too much
+> concentration or reflection to decipher the non-monotonic and discontinuous
+> nature of the two scales.
+
+Added to the reference list as `isenberg2011`. Venue/DOI still needed — queued.
+
+Also flagged: Stephen Few, *Dual-Scaled Axes in Graphs: Are They Ever the Best
+Solution?*, which per Datawrapper concludes he "cannot think of a situation that
+warrants them in light of other, better solutions." Added as `fewdualaxes`;
+year and URL queued.
+
+## Alternatives they give
+
+Side-by-side charts; indexed charts (with Knaflic's caveat that this fails when
+one series moves +10000% and the other +5%); prioritise-and-label; connected
+scatterplot. Our page's repentance already recommends the first two.

docs/sources/datawrapper-pie-and-stacked.md

@@ -0,0 +1,71 @@
+# Datawrapper — pie charts, and stacked column charts
+
+Two "Data vis do's & don'ts" posts by Lisa Charlotte Muth, supplied as PDFs and
+read 2026-08-05. Answers queue rows 1 and 6.
+
+## "What to consider when creating pie charts" (3 Jan 2018)
+
+`blog.datawrapper.de/pie-charts/`
+
+Opening line, which is the useful one for us:
+
+> Pie charts are great to show how 100% divide up into a few shares.
+
+And the positive case, which we should keep quoting when people accuse us of
+banning the form:
+
+> Pie charts work best for values around 25%, 50% or 75%. It's easier for
+> readers to spot these percentages in a pie chart than in a stacked bar or
+> column chart.
+
+Then the limits, both of which are our published sin:
+
+> Pie charts are not the best choice if you want readers to compare the size of
+> shares. That's especially true if the differences between the shares are
+> small.
+
+> Pie charts work best if you only have a few values – five max.
+
+Our sample pie has eight. Cited on `too-many-pie-slices`.
+
+**Caveat for backlog entries 2 and 3.** This post does *not* say in so many
+words that a pie's parts must be mutually exclusive and sum to 100% — so it
+can't be cited for the multi-select survey case as I'd hoped. What it does give
+is the premise those sins rest on:
+
+> One pie chart can only show one total and its shares.
+
+That's enough to support "there must be a whole," and nothing more. The backlog
+notes now say exactly that rather than implying a stronger claim.
+
+Also flags Robert Kosara's "Understanding Pie Charts" as a "great research-based
+explanation of how people read pie charts" — independent support for our
+`eagereyes` note (claim V7). URL not captured; queued.
+
+## "What to consider when creating stacked column charts" (13 Feb 2018)
+
+`blog.datawrapper.de/stacked-column-charts/`
+
+The sentence backlog entry 6 is built on:
+
+> It's hard for readers to compare columns that don't start at the same
+> baseline.
+
+And the design guidance:
+
+> Bring the most important value to the bottom of the chart and use color to
+> make it stand out. Your readers can compare values easier with each other if
+> they have the same baseline.
+
+Useful nuance I hadn't accounted for: a 100%-stacked chart has **two** usable
+baselines, not one —
+
+> You will gain a second baseline at the top of your chart where you can place
+> the second most important category in your data.
+
+So the sin is sharper than "only the bottom is readable": everything *between*
+the two baselines floats. Worth writing the page that way.
+
+Also: "Make sure that you include all parts of the total in your charts – and
+only parts of the total." Pairs with the Economist's rainbow-stack example,
+where a partial selection of countries was stacked as if it were the whole.

docs/sources/economist-mistakes.md

@@ -0,0 +1,53 @@
+# The Economist — "Mistakes, we've drawn a few"
+
+Sarah Leo, visual data journalist at The Economist. Supplied as PDF, read
+2026-08-05. Answers queue row 4 (content; the canonical URL is still missing)
+and settles claim **V1**.
+
+Structure: her own archive, grouped into charts that are (1) misleading,
+(2) confusing, (3) failing to make a point, each with a redesign at comparable
+size.
+
+## V1 — confirmed, but our note over-claimed it
+
+The article's **first** example is `Mistake: Truncating the scale` — a bar chart
+of average Facebook likes on posts by the political left:
+
+> The original chart not only downplays the number of Mr Corbyn's likes but also
+> exaggerates those on other posts.
+
+So the sin is there, and it leads the piece. But it is **one** example, not the
+"several" our citation note claimed. Note corrected to match. (She does add
+that "avid followers of this blog will have seen another example of this bad
+practice," which is a pointer elsewhere, not a second case here.)
+
+## Unexpected: it contains our dual-axis sin, self-caught
+
+`Mistake: Forcing a relationship by cherry-picking scales` — dog weights against
+neck sizes, on a dual axis, looking perfectly correlated:
+
+> In the original chart, both scales decrease by three units (from 21 to 18 on
+> the left; from 45 to 42 on the right). In percentage terms, the left scale
+> decreases by 14% while the right goes down by 7%.
+
+Her takeaway is quotable and close to our page's:
+
+> if two series follow each other too closely, it is probably a good idea to
+> have a closer look at the scales.
+
+Added as a citation on `dual-axis-correlation`. A newsroom catching itself doing
+the exact thing is stronger evidence than a textbook saying not to.
+
+## Also useful
+
+- `Taking the "mind-stretch" a little too far` — trade deficit vs manufacturing
+  employment, where "the two data series don't share a common baseline. The
+  baseline of the trade deficit is at the top of the chart." Adjacent to our
+  inverted-axis sin without being a case of it; cite carefully if at all.
+- `Including too much detail` ("What a rainbow!") — stacked areas for ten
+  countries. Her point that "since we are not plotting all euro-area countries
+  it doesn't make any sense to stack the data" is a good detail for the stacked
+  backlog entry: stacking implies the parts are the whole.
+- She cites Francis Gagnon's rule of thumb — leave at least 33% of the plot area
+  free under a line chart that doesn't start at zero. No source captured; not
+  cited anywhere yet.